Question: A direct search technique to tackle the economic dispatch issue featuring valve-point characteristics.

In this paper, an innovative technique, termed Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), is introduced for the automation of recurrent neural/fuzzy network design. This method integrates genetic algorithms (GA) and particle swarm optimization (PSO) to form a novel evolutionary learning algorithm. In HGAPSO, the creation of new generation individuals is not limited to the traditional crossover and mutation operations found in GA; it also includes PSO. A key feature of HGAPSO is the elite strategy, where the top-performing half of the population is identified as elites. These elites are not directly replicated in the subsequent generation; instead, they are enhanced. They are grouped into a swarm, with each elite acting as a particle within the swarm. Through the application of PSO, these elites undergo a maturation process akin to that observed in nature. The enhanced elites then make up half of the population in the new generation, while the remaining half is produced through crossover and mutation operations applied to these refined elites. The effectiveness of HGAPSO in recurrent neural/fuzzy network design is demonstrated through its application. For recurrent neural networks, a fully connected structure is utilized to address a temporal sequence production problem. Regarding recurrent fuzzy network design, a Takagi-Sugeno-Kang-type network is employed for dynamic plant control. Comparative studies with traditional GA and PSO methods highlight the superior performance of HGAPSO in these recurrent network design tasks.

This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.

Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ‘‘complexity number’’ for the control system. # 2003 Elsevier Ltd. All rights reserved.

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research. OPEN ACCESS Energies 2014, 7 6931

As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration will cause overload conditions at the distribution transformer. This paper focuses on the impacts of charging EVs on residential distribution networks including the transformer. The cost to accommodate a large-scale EV penetration by upgrading distribution transformers can be prohibitive. To alleviate the potential new load peaks with minimal infrastructure investments, a demand response strategy is proposed as a load shaping tool that allows improvement in distribution transformer usage. With the proposed strategy, consumers' preferences, load priorities, and privacy can be taken into account.

This is an updated version of Chapter 4 of the author’s Dynamic Programming and Optimal Control, Vol. II, 4th Edition, Athena Scientific, 2012. It includes new material, and it is substantially revised and expanded (it has more than doubled in size). The new material aims to provide a unified treatment of several models, all of which lack the contractive structure that is characteristic of the discounted problems of Chapters 1 and 2: positive and negative cost models, deterministic optimal control (including adaptive DP), stochastic shortest path models, and risk-sensitive models. Here is a summary of the new material:

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system

After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.

In this paper, a three-level Boost (TLB) converter maximum power point tracking (MPPT) control strategy for a two-stage photovoltaic (PV) system based on model predictive control (MPC) is proposed. This method realizes fast and precise control through establishment of a prediction model and minimization of a cost function. In the work, first a three-level predictive model for the boost converter is extracted, then a predictive control algorithm using the Perturb and Observe (P&O) method is proposed, and subsequently voltage balance of the neutral point of the converter is added to the control loop. Based on the proposed method, since the inverter is not obligated to control the DC-link neutral point voltage, its modulation can be used to realize other controls on the AC side to improve grid operation. Results of the control scheme have been verified through Matlab / Simulink, and the results have been in agreement with the expectations.

This paper is a synthesis of the control circuit in an induction heating system with three-phase fully-controlled rectifier, single-phase voltage source inverter and L-LC resonant load. The attention is mainly directed on the control of the power transmitted to the induction coil. It is highlighted that the analytical design of a proportional integral derivative or proportional integral controller is practically impossible when the direct control of the current through inductor is desired. The two possibilities of power control taken into consideration are the inverter output current control and the inverter input voltage control. The design of the current and voltage controllers of proportional integral derivative type was carried out successfully based on Modulus Optimum criterion in Kessler variant. The performances of the control system in both variants of the power control were tested and validated by simulation under the Matlab-Simulink environment by using real parameters of induction coils and heated pipes from a leading Romanian manufacturer.


Question: A direct search technique for tackling the economic dispatch problem incorporating valve-point characteristics.

This document introduces an innovative hybrid approach for addressing the Dynamic Economic Dispatch (DED) problem, a critical aspect of power generation management and control. DED aims to establish the most cost-effective settings for generator units in anticipation of forecasted load demands over a specified timeframe, all while ensuring the power system operates within its security boundaries. The proposed methodology combines the strengths of Evolutionary Programming (EP) and Sequential Quadratic Programming (SQP) to tackle the DED challenge. Initially, a rudimentary EP algorithm is employed as the primary search mechanism, guiding the search towards the optimal global solution. Subsequently, a fine-tuning process using SQP is implemented to precisely identify the optimal solution in the concluding stages. The efficacy of this hybrid method is demonstrated through its application to a test system comprising ten units, featuring a nonsmooth fuel cost function. Comparative analysis with results obtained from standalone EP and SQP methods underscores the superior performance of the proposed hybrid technique in solving DED problems.

The test and the characterization of medium or high-power electronic converters, under nominal operating conditions, are made difficult by the requirement of high-power electrical source and load. In addition, the energy lost during the test may be very significant. The opposition method, which consists of an association of two identical converters supplied by the same source, one operating as a generator, the other as a receptor, can be a better way to do these test. Another advantage is the possibility to realize accurate measurements of the different losses in the converters under test. In the first part of this paper, the characteristics of the method concerning loss measurements are compared to those of the electrical or calorimetric methods, then it is shown how it can be applied to different types of power electronic converters, choppers, switched mode power supplies, and pulsewidth modulation inverters. In the second part, different examples of studies conducted by the authors, and using this method, are presented. They have varying goals, from the test of soft-switching inverters to the characterization of integrated gate-commutated thyristor (IGCT) devices mounted into 2-MW choppers.

Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.

This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.

Abstract: This paper generalises the golden section optimal search method to higher dimensional optimisation problem. The method is applicable to a strict quasi-convex function of N-variables over an N-dimensional hyper rectangle. An algorithm is proposed in N-dimension. The algorithm is illustrated graphically in two dimensions and verified through several test functions in higher dimension using MATLAB.

Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

Converteam recently has introduced the MV7000 series of medium voltage drives, designed with Press Pack IGBT (PPI) in 3-level NPC topology in the power range from 4 MW to 32 MW [3]. PPI combines the benefits of disk type device technology with state-of-the-art semiconductor technology. This paper explains relevant technical features and design issues related to MV7000 including direct series connection of devices.

This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.

A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed. 2008 Elsevier Ltd. All rights reserved.

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or tness, to such candidate solutions. After reviewing current evolutionary approaches to multiobjective and constrained optimization, the paper proposes that tness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The e ect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.


Question: A direct search technique for tackling the economic dispatch problem incorporating valve-point effects.

This document delves into Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, a topic of significant interest in the realm of artificial intelligence. It's not uncommon to find yourself at a loss when seeking out this book in local bookstores, given its high demand and swift sales. This scarcity often leads to frustration when you're unable to locate a copy. However, the advent of digital platforms has revolutionized the way we access literature, particularly sought-after titles like this one. By exploring online resources, you can ensure that you won't miss out on acquiring this popular book. This digital availability offers a convenient solution, sparing you the disappointment of finding the book sold out in physical stores.

It's worth noting that the Genetic Fuzzy Systems methodology, as discussed in this book, is centered around the evolutionary tuning and learning of fuzzy knowledge bases. This approach combines the principles of genetic algorithms with fuzzy logic to create adaptive systems capable of learning and refining their knowledge bases over time. The methodology is particularly useful in complex environments where uncertainty and imprecision are prevalent, making it a valuable resource for researchers and practitioners in the field of artificial intelligence. The book's exploration of this topic is comprehensive, covering both theoretical foundations and practical applications, making it an indispensable addition to any AI enthusiast's library.

In this paper, a method to improve efficiency in half-bridge series resonant inverter applied to domestic induction heating is presented. Low and medium output powers required for this application implies the use of higher switching frequencies, which leads to an efficiency decrease. Asymmetrical duty cycle (ADC) modulation scheme is proposed to improve efficiency due to its switching frequency reduction and absence of additional hardware requirements. Study methodology comprises, in a first step, a theoretical analysis of power balance as a function of control parameters: duty cycle and switching frequency. In addition, restrictions due to snubber and dead time, and variability of the induction loads have been considered. Afterwards, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine global importance of each one for different switching devices. ADC modulation efficiency improvement is achieved by means of a switching frequency reduction, mainly at low-medium power range and low quality factor (Q) loads. The analytical results obtained with this study have been validated through an induction heating test-bench. A discrete 3-kW RL load has been designed to emulate a typical induction heating load. Then, a commercial induction heating inverter is used to evaluate ADC modulation scheme.

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-words: Simulated annealing; Nonconvex optimization; Gradient descent; Generalized Statistical Mechanics.

In this paper, we present a new Integer Program (IP) for the Air Traffic Flow Management (ATFM) problem. The model we propose provides a complete representation of all the phases of each flights, i.e., the phase of taking-off, of cruising and of landing; suggesting all the actions to be implemented to achieve the goal of safe, efficient, and expeditious aircraft movement. The distinctive feature of the model is that it allows rerouting decisions. These decisions are formulated by means of “local” conditions, which allow us to represent such decisions in a very compact way by only introducing new constraints. Moreover, to strengthen the polyhedral structure of the underlying relaxation, we also present three classes of valid inequalities. We report short computational times (less than 15 minutes) on instances of the size of the US air traffic control system that make it realistic that our approach can be used as the main engine of managing air traffic in the US.

The numerical solution of nonlinear integral equations involves the iterative soIutioon of finite systems of nonlinear algebraic or transcendental equations. Certain corwent i o n a l techniqucs for treating such systems are reviewed in the context of a particular class of n o n l i n e a r equations. A procedure is synthesized to offset some of the disadvantages of these t e c h n i q u e s in this context; however, the procedure is not restricted to this pt~rticular class of s y s t e m s of nonlinear equations.

Electrical power generation is changing dramatically across the world because of the need to reduce greenhouse gas emissions and to introduce mixed energy sources. The power network faces great challenges in transmission and distribution to meet demand with unpredictable daily and seasonal variations. Electrical Energy Storage (EES) is recognized as underpinning technologies to have great potential in meeting these challenges, whereby energy is stored in a certain state, according to the technology used, and is converted to electrical energy when needed. However, the wide variety of options and complex characteristic matrices make it difficult to appraise a specific EES technology for a particular application. This paper intends to mitigate this problem by providing a comprehensive and clear picture of the state-of-the-art technologies available, and where they would be suited for integration into a power generation and distribution system. The paper starts with an overview of the operation principles, technical and economic performance features and the current research and development of important EES technologies, sorted into six main categories based on the types of energy stored. Following this, a comprehensive comparison and an application potential analysis of the reviewed technologies are presented. 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. It is also considered as the class of the NPcomplete combinatorial optimization problems. By literatures, many algorithms and approaches have been launched to solve such the TSP. However, no current algorithms that can provide the exactly optimal solution of the TSP problem are available. This paper proposes the application of AI search techniques to solve the TSP problems. Three AI search methods, i.e. genetic algorithms (GA), tabu search (TS), and adaptive tabu search (ATS), are conducted. They are tested against ten benchmark real-world TSP problems. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems. Key-Words: Traveling Salesman Problem, Genetic Algorithm, Tabu Search, Adaptive Tabu Search

We present a method for optimizing and automating component and transistor sizing for CMOS operational amplifiers. We observe that a wide variety of performance measures can be formulated as posynomial functions of the design variables. As a result, amplifier design problems can be formulated as a geometric program, a special type of convex optimization problem for which very efficient global optimization methods have recently been developed. The synthesis method is therefore fast, and determines the globally optimal design; in particular the final solution is completely independent of the starting point (which can even be infeasible), and infeasible specifications are unambiguously detected. After briefly introducing the method, which is described in more detail by M. Hershenson et al., we show how the method can be applied to six common op-amp architectures, and give several example designs.

This paper presents a recurrent neural circuit for solving quadratic programming problems. The objective is tominimize a quadratic cost function subject to linearconstraints. The proposed circuit employs non-linearfeedback, in the form of unipolar comparators, to introducetranscendental terms in the energy function ensuring fastconvergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit comparesfavorably with other proposed circuits for the same task. PSPICE simulation results arepresented for a chosen optimization problem and are foundto agree with the algebraic solution.

This paper presents a digital nonlinearity calibration technique for ADCs with strong input–output discontinuities between adjacent codes, such as pipeline, algorithmic, and SAR ADCs with redundancy. In this kind of converter, the ADC transfer function often involves multivalued regions, where conventional integral-nonlinearity (INL)-based calibration methods tend to miscalibrate, negatively affecting the ADC’s performance. As a solution to this problem, this paper proposes a novel INL-based calibration which incorporates information from the ADC’s internal signals to provide a robust estimation of static nonlinear errors for multivalued ADCs. The method is fully generalizable and can be applied to any existing design as long as there is access to internal digital signals. In pipeline or subranging ADCs, this implies access to partial subcodes before digital correction; for algorithmic or SAR ADCs, conversion bit/bits per cycle are used. As a proof-of-concept demonstrator, the experimental results for a 1.2 V 23 mW 130 nm-CMOS pipeline ADC with a SINAD of 58.4 dBc (in nominal conditions without calibration) is considered. In a stressed situation with 0.95 V of supply, the ADC has SINAD values of 47.8 dBc and 56.1 dBc, respectively, before and after calibration (total power consumption, including the calibration logic, being 15.4 mW).

The modeling and simulation of the electrified transit system is an essential element in the design process of a new railway, or an existing one being modernized, particularly in DC powered railway systems which have significant losses in the power network. With the continuing focus on environmental concerns and rising energy prices, energy-saving operation technology for railway systems has been paid more and more attention. Previous work on energy optimization techniques mainly focuses on optimizing driving strategies subject to geographic and physical constraints, and kinematic equations, which only minimizes the mechanical energy consumption without considering the loss from the power supply network. This paper proposes a DC power network modeling technique and extends the traditional energy-saving methods to develop a novel approach which combines traction power supply network calculations and numerical algorithms to minimize the electrical energy delivered from substations. As train resistance is time-varying with the train movement, iterative algorithms are presented in order to calculate the energy consumption dynamically. Some case studies based on the Beijing Yizhuang Subway Line are presented to illustrate the proposed approach for power network simulation and energy-saving, in which the energy consumption of both the practical operation and optimal operation are compared.


Question: A direct search technique for tackling the economic dispatch issue with valve-point characteristics.

This document proposes an enhanced version of the Particle Swarm Optimization (PSO) algorithm, featuring the introduction of an inertia weight parameter. Through comprehensive simulations, the study underscores the substantial and positive influence of this novel parameter on the performance of the PSO algorithm. The inclusion of inertia weight is shown to refine the balance between exploration and exploitation, leading to improved convergence and optimization outcomes. This modified PSO, with its enhanced capabilities, offers a more effective solution approach for complex optimization problems, demonstrating its superiority over the original PSO formulation.

Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.

We present a method for optimizing and automating component and transistor sizing for CMOS operational amplifiers. We observe that a wide variety of performance measures can be formulated as posynomial functions of the design variables. As a result, amplifier design problems can be formulated as a geometric program, a special type of convex optimization problem for which very efficient global optimization methods have recently been developed. The synthesis method is therefore fast, and determines the globally optimal design; in particular the final solution is completely independent of the starting point (which can even be infeasible), and infeasible specifications are unambiguously detected. After briefly introducing the method, which is described in more detail by M. Hershenson et al., we show how the method can be applied to six common op-amp architectures, and give several example designs.

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.02.075 ⇑ Corresponding author. Tel.: +4773597119. E-mail addresses: hanhong@stud.ntnu.no (H (Y. Wang), Kesheng.wang@ntnu.no (K. Wang), (Y. Chen). One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers. 2011 Elsevier Ltd. All rights reserved.

We propose a simulation based algorithm for optimizing the average reward in a Markov Reward Process that depends on a set of parameters As a special case the method applies to Markov Decision Processes where optimization takes place within a parametrized set of policies The algorithm involves the simulation of a single sample path and can be implemented on line A convergence result with probability is provided This research was supported by contracts with Siemens AG Munich Germany and Alcatel Bell Belgium and by contract DMI with the National Science Foundation Introduction Markov Decision Processes and the associated dynamic programming DP methodology Ber a Put provide a general framework for posing and analyzing problems of se quential decision making under uncertainty DP methods rely on a suitably de ned value function that has to be computed for every state in the state space However many inter esting problems involve very large state spaces curse of dimensionality In addition DP assumes the availability of an exact model in the form of transition probabilities In many practical situations such a model is not available and one must resort to simulation or experimentation with an actual system For all of these reasons dynamic programming in its pure form may be inapplicable The e orts to overcome the aforementioned di culties involve two main ideas The use of simulation to estimate quantities of interest thus avoiding model based computations The use of parametric representations to overcome the curse of dimensionality Parametric representations and the associated algorithms can be broadly classi ed into three main categories a Parametrized value functions Instead of associating a value V i with each state i one uses a parametric form V i r where r is a vector of tunable parameters weights and V is a so called approximation architecture For example V i r could be the output of a multilayer perceptron with weights r when the input is i Other representations are possible e g involving polynomials linear combina tions of feature vectors state aggregation etc When the main ideas from DP are combined with such parametric representations one obtains methods that go un der the names of reinforcement learning or neuro dynamic programming see BT SB for textbook expositions as well as the references therein A key char acteristic is that policy optimization is carried out in an indirect fashion one tries to obtain a good approximation of the optimal value function of dynamic programming and uses it to construct policies that are close to optimal Such methods are reason ably well though not fully understood and there have been some notable practical successes see BT SB for an overview including the world class backgammon player by Tesauro Tes b Parametrized policies In an alternative approach which is the one considered in this paper the tuning of a parametrized value function is bypassed Instead one considers a class of policies described in terms of a parameter vector Simulation is employed to estimate the gradient of the performance metric with respect to and the policy is improved by updating in a gradient direction In some cases the re quired gradient can be estimated using IPA in nitesimal perturbation analysis see e g HC Gla CR and the references therein For general Markov processes and in the absence of special structure IPA is inapplicable but gradient estimation is still possible using likelihood ratio methods Gly Gly GG LEc GI c Actor critic methods A third approach which is a combination of the rst two includes parametrizations of the policy actor and of the value function critic BSA While such methods seem particularly promising theoretical understand ing has been limited to the impractical case of lookup representations one parameter per state KB This paper concentrates on methods based on policy parametrization and approx imate gradient improvement in the spirit of item b above While we are primarily interested in the case of Markov Decision Processes almost everything applies to the more general case of Markov Reward Processes that depend on a parameter vector and we proceed within this broader context We start with a formula for the gradient of the performance metric that has been presented in di erent forms and for various contexts in Gly CC FH JSJ TH CW We then suggest a method for estimating the terms that appear in that formula This leads to a simulation based method that updates the parameter vector at every regeneration time in an approximate gradient direction Furthermore we show how to construct an on line method that updates the parameter vector at each time step The resulting method has some conceptual similarities with those described in CR that reference assumes however the availability of an IPA estimator with certain guaranteed properties that are absent in our context and in JSJ which however does not contain convergence results The method that we propose only keeps in memory and updates K numbers where K is the dimension of Other than itself this includes a vector similar to the eligibility trace in Sutton s temporal di erence methods and as in JSJ an estimate of the average reward under the current value of If that estimate was accurate our method would be a standard stochastic gradient algorithm However as keeps changing is generally a biased estimate of the true average reward and the mathematical structure of our method is more complex than that of stochastic gradient algorithms For reasons that will become clearer later standard approaches e g martingale arguments or the ODE approach do not seem to su ce for establishing convergence and a more elaborate proof is necessary Our gradient estimator can also be derived or interpreted in terms of likelihood ratios Gly GG It takes the same form as the one presented in p of Gly but it is used di erently The development in Gly leads to a consistent estimator of the gradient assuming that a very large number of regenerative cycles are estimated while keeping the policy parameter at a xed value Presumably would be then updated after such a long simulation In contrast our method updates much more frequently and retains the desired convergence properties despite the fact that any single cycle results in a biased gradient estimate An alternative simulation based stochastic gradient method again based on a likeli hood ratio formula has been provided in Gly and uses the simulation of two regen erative cycles to construct an unbiased estimate of the gradient We note some of the di erences with the latter work First the methods in Gly involve a larger number of auxiliary quantities that are propagated in the course of a regenerative cycle Second our method admits a modi cation see Sections that can make it applicable even if the time until the next regeneration is excessive in which case likelihood ratio based methods su er from excessive variance Third our estimate  of the average reward is obtained as a weighted average of all past rewards not just over the last regenerative cycle In contrast an approach such as the one in Gly would construct an independent estimate of  during each regenerative cycle which should result in higher variance Finally our method brings forth and makes crucial use of the value di erential reward function of dy namic programming This is important because it paves the way for actor critic methods in which the variance associated with the estimates of the di erential rewards is poten tially reduced by means of learning value function approximation Indeed subsequent to the rst writing of this paper this latter approach has been pursued in KT SMS In summary the main contributions of this paper are as follows We introduce a new algorithm for updating the parameters of a Markov Reward Process on the basis of a single sample path The parameter updates can take place either during visits to a certain recurrent state or at every time step We also specialize the method to Markov Decision Processes with parametrically represented policies In this case the method does not require the transition probabilities to be known We establish that the gradient with respect to the parameter vector of the perfor mance metric converges to zero with probability which is the strongest possible result for gradient related stochastic approximation algorithms The method admits approximate variants with reduced variance such as the one described in Section or various types of actor critic methods The remainder of this paper is organized as follows In Section we introduce our framework and assumptions and state some background results including a formula for the gradient of the performance metric In Section we present an algorithm that per forms updates during visits to a certain recurrent state present our main convergence result and provide a heuristic argument Sections and deal with variants of the algo rithm that perform updates at every time step In Section we specialize our methods to the case of Markov Decision Processes that are optimized within a possibly restricted set of parametrically represented randomized policies We present some numerical results in Section and conclude in Section The lengthy proof of our main results is developed in the appendices Markov Reward Processes Depending on a Parameter In this section we present our general framework make a few assumptions and state some basic results that will be needed later We consider a discrete time nite state Markov chain fing with state space S f Ng whose transition probabilities depend on a parameter vector K and are denoted by pij P in j j in i Whenever the state is equal to i we receive a one stage reward that also depends on and is denoted by gi For every K let P be the stochastic matrix with entries pij Let P fP j Kg be the set of all such matrices and let P be its closure Note that every element of P is also a stochastic matrix and therefore de nes a Markov chain on the same s

-Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.

This paper presents a new control method for cascaded connected H-bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of power semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-bridges of the converter

The rapidly growing use of digital control in Distributed Power Architectures (DPA) has been primarily driven by the increasing need of sophisticated Power Management functions. However, specifically designed control algorithms can also be exploited for optimizing the system performance. In this paper we present a new auto-tuning method and system that makes it possible to operate an isolated DC-DC converter at maximum efficiency. The auto-tuning performs an optimum adjustment of both primary side dead time and secondary side conduction times based on the measurement of the input current. It does not require any additional external components since current sensing functions are already implemented for power management purposes. Experimental measurements performed on an asymmetrical driven half-bridge DC-DC converter demonstrate the effectiveness of our solution and its robustness to component variations.

Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.

A photovoltaic (PV) power conditioning system (PCS) with line connection is proposed. Using the power slope versus voltage of the PV array, the maximum power point tracking (MPPT) controller that produces a smooth transition to the maximum power point is proposed. The dc current of the PV array is estimated without using a dc current sensor. A current controller is suggested to provide power to the line with an almost-unity power factor that is derived using the feedback linearization concept. The disturbance of the line voltage is detected using a fast sensing technique. All control functions are implemented in software with a single-chip microcontroller. Experimental results obtained on a 2-kW prototype show high performance such as an almost-unity power factor, a power efficiency of 94%, and a total harmonic distortion (THD) of 3.6%

Many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users. This paper concerns a class of resource allocation problems whose objective function depends on the aggregate allocation (i.e., the sum of individual allocations); in particular, we investigate distributed algorithmic solutions that preserve the privacy of participating users. Without privacy considerations, existing distributed algorithms normally consist of a central entity computing and broadcasting certain public coordination signals to participating users. However, the coordination signals often depend on user information, so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk. We present a distributed optimization algorithm that preserves differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have. The algorithm achieves privacy by perturbing the public signals with additive noise, whose magnitude is determined by the sensitivity of the projection operation onto user-specified constraints. By viewing the differentially private algorithm as an implementation of stochastic gradient descent, we are able to derive a bound for the suboptimality of the algorithm. We illustrate the implementation of our algorithm via a case study of electric vehicle charging. Specifically, we derive the sensitivity and present numerical simulations for the algorithm. Through numerical simulations, we are able to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and the trade-off between privacy level and suboptimality.


Question: An application of direct search methodology in solving economic dispatch issues with valve-point characteristics.

This article presents a novel recurrent fuzzy neural network (RFNN) framework designed for the identification and control of nonlinear dynamic systems. The RFNN architecture is fundamentally a recurrent multilayer connectionist network tailored to implement fuzzy inference through dynamic fuzzy rules. By integrating feedback connections in the second layer of the fuzzy neural network (FNN), the RFNN effectively captures temporal relationships, thereby enhancing the FNN's basic capability to address time-series problems. Moreover, the paper extends the established results for FNN's fuzzy inference engine, universal approximation, and convergence analysis to the RFNN context. Regarding the control aspect, the article discusses both direct and indirect adaptive control strategies utilizing the RFNN. Rigorous proofs, grounded in the Lyapunov stability theory, are provided to ensure the convergence of the RFNN by selecting suitable learning rates. The efficacy of the RFNN is substantiated through various simulation studies, encompassing time series prediction, identification, and control of complex nonlinear systems, thereby validating its robust performance.

Nearly all science and engineering fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highestperforming solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how highperforming solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their costto-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAPElites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves the state-of-the-art for finding a single, best solution, it will catalyze advances throughout all science and engineering fields.

As the number of plug-in hybrid vehicles (PHEVs) increases, so might the impacts on the power system performance, such as overloading, reduced efficiency, power quality, and voltage regulation particularly at the distribution level. Coordinated charging of PHEVs is a possible solution to these problems. In this work, the relationship between feeder losses, load factor, and load variance is explored in the context of coordinated PHEV charging. From these relationships, three optimal charging algorithms are developed which minimize the impacts of PHEV charging on the connected distribution system. The application of the algorithms to two test systems verifies these relationships approximately hold independent of system topology. They also show the additional benefits of reduced computation time and problem convexity when using load factor or load variance as the objective function rather than system losses. This is important for real-time dispatching of PHEVs.

Enterprise-wide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimal operation of manufacturing facilities, which often requires the use of nonlinear process models. Major operational items include planning, scheduling, realtime optimization and inventory control. One of the key features of EWO is integration of the information and the decision-making among the various functions that comprise the supply chain of the company. This can be achieved with modern IT tools, which together with the internet, have promoted e-commerce. However, as will be discussed, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high levels of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational). © 2005 American Institute of Chemical Engineers AIChE J, 51: 1846–1857, 2005

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.

This work is motivated by the experimental design problem of optimizing the power output of nano-enhanced microbial fuel cells. Microbial fuel cells (MFCs) (Bond and Lovley, 2003; Fan et al., 2007; Park and Zeikus, 2003; Reguera, 2005) use micro-organisms to break down organic matter and generate electricity. For a particular MFC design, it is critical to optimize the biological energetics and the microbial/electrode interface of the system, which research has shown to depend strongly on the surface properties of the anodes (Park and Zeikus, 2003; Reguera, 2005). This motivates the design of nano-enhanced anodes, where nano-structures (e.g. carbon nano-wire) are grown on the anode surface to improve the MFC’s power output. Unfortunately, there is little understanding of the interaction between various possible nano-enhancements and MFC capabilities for different micro-organisms. Thus, optimizing anode design for a particular application is largely guess work. Our goal is to develop algorithms to aid this process. Bayesian optimization (Jones, 2001a; Brochu et al., 2009) has been widely used for experimental design problems where the goal is to optimize an unknown function f(·), that is costly to evaluate. In general, we are interested in finding the point x∗ ∈ X d ⊂ Rd such that: x∗ = argmax x∈X d f(x), (1)

This paper presents a multi-objective Model Predictive Control (MPC) for grid-tied 4-cell 15-level Packed U Cells (PUC) inverter. This challenging topology is characterized by high power quality with reduced number of components compared to conventional multilevel inverters. Compared to traditional PI controller, MPC is attracting more interest due to its good dynamic response and high accuracy of reference tracking, through the minimization of a flexible user-defined cost function. For the presented PUC topology, the grid current should be jointly controlled with the capacitors' voltages for ensuring proper operation of the inverter, which leads to an additional requirement of pre-charge circuits for the capacitors in case of using PI current controller (or using additional PI controllers). The proposed MPC achieves grid current injection, low current THD, unity PF, while balancing the capacitors' voltages.

Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multiobjective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the S. Ravizza ( ) · J.A.D. Atkin School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK e-mail: smr@cs.nott.ac.uk J.A.D. Atkin e-mail: jaa@cs.nott.ac.uk J. Chen · P. Stewart School of Engineering, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK J. Chen e-mail: juchen@lincoln.ac.uk P. Stewart e-mail: pstewart@lincoln.ac.uk E.K. Burke Department of Computing and Mathematics, University of Stirling, Cottrell Building, Stirling FK9 4LA, UK e-mail: e.k.burke@stir.ac.uk 26 S. Ravizza et al. trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.

To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.

Any piecewise smooth function that is specified by an evaluation procedure involving smooth elemental functions and piecewise linear functions like min and max can be represented in the so-called abs-normal form. By an extension of algorithmic, or automatic, differentiation, one can then compute certain first and second order derivative vectors and matrices that represent a local piecewise linearization and provide additional curvature information. On the basis of these quantities we characterize local optimality by first and second order necessary and sufficient conditions, which generalize the corresponding KKT theory for smooth problems. The key assumption is the Linear Independence Kink Qualification (LIKQ), a generalization of LICQ familiar from nonlinear optimization. It implies that the objective has locally a so-called VU decomposition and renders everything tractable in terms of matrix factorizations and other simple linear algebra operations. By yielding descent directions whenever they are violated the new optimality conditions point the way to a superlinearly convergent generalized QP solver, which is currently under development. We exemplify the theory on two nonsmooth examples of Nesterov.

The paper presents a clustering based approach for consumers’ classification in representative categories characterized by typical load profiles. For determination of the consumption categories, every customer must be characterized by the following primary information: daily (monthly) energy consumption, average load and peak load. The used database contains the daily load curves corresponding to the small customers from a rural distribution system from Romania. The obtained results it demonstrated that the proposed approach can be used with the success in building specific tariff structures of the customers or in the optimal operation and planning of distribution systems.


Question: Sentiment analysis of financial microblogs for bearish and bullish market trends.

This paper outlines the SemEval-2015 Task 11, which focuses on sentiment analysis specifically targeting figurative language found in Twitter posts. As the inaugural task to exclusively concentrate on this aspect of sentiment analysis, it aims to decipher three primary forms of figurative language: irony, sarcasm, and metaphor. To facilitate this analysis, a comprehensive dataset was meticulously curated, consisting of 8000 tweets for training and 4000 for testing, all meticulously annotated by contributors on the CrowdFlower platform. Systems participating in the task were tasked with assigning a detailed sentiment score on an 11-point scale (-5 to +5, with 0 denoting a neutral sentiment) to each tweet. Evaluation of the systems' performance was conducted using dual metrics: Cosine similarity and Mean-Squared-Error, ensuring a thorough assessment of their accuracy in sentiment detection.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
An innovative evolutionary recurrent network design methodology is presented, which integrates genetic algorithms (GA) and particle swarm optimization (PSO) to automate the design of recurrent neural and fuzzy networks. This novel approach, termed Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), combines crossover and mutation operations from GA with the optimization capabilities of PSO. The strategy employs an elite approach, where the upper echelon of high-performing individuals is considered for enhancement through PSO, mirroring natural maturation processes. These enhanced elites constitute a significant portion of the new generation, with the rest created through traditional GA operations. HGAPSO is successfully applied to the design of recurrent neural and fuzzy networks, demonstrating its effectiveness in addressing complex network design challenges. Comparative analysis with traditional GA and PSO methodologies showcases the superior performance of HGAPSO in recurrent network design.

**Similar Documents**: This paper introduces the Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) method, a groundbreaking technique for automating the design of recurrent neural and fuzzy networks. By merging the strengths of genetic algorithms (GA) and particle swarm optimization (PSO), HGAPSO offers a unique evolutionary learning algorithm. Unlike conventional approaches, HGAPSO utilizes both crossover and mutation operations from GA and the optimization capabilities of PSO. The method employs an elite strategy, where the top-performing individuals are not merely replicated but are first enhanced using PSO, which simulates the maturation process in nature. These enhanced elites form half

Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.

BACKGROUND
Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

OBJECTIVE
The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

METHODS
We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

RESULTS
The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

CONCLUSIONS
Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach. While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labelling Facebook posts with their associated sentiments.

Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.

We comment on Eichstaedt et al.'s (2015a) claim to have shown that language patterns among Twitter users, aggregated at the level of US counties, predicted county-level mortality rates from atherosclerotic heart disease (AHD), with "negative" language being associated with higher rates of death from AHD and "positive" language associated with lower rates. First, we examine some of Eichstaedt et al.'s apparent assumptions about the nature of AHD, as well as some issues related to the secondary analysis of online data and to considering counties as communities. Next, using the data files supplied by Eichstaedt et al., we reproduce their regression- and correlation-based models, substituting mortality from an alternative cause of death-namely, suicide-as the outcome variable, and observe that the purported associations between "negative" and "positive" language and mortality are reversed when suicide is used as the outcome variable. We identify numerous other conceptual and methodological limitations that call into question the robustness and generalizability of Eichstaedt et al.'s claims, even when these are based on the results of their ridge regression/machine learning model. We conclude that there is no good evidence that analyzing Twitter data in bulk in this way can add anything useful to our ability to understand geographical variation in AHD mortality rates.

As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.

The popularity of social media in recent years has increased a lot and created new opportunities to study the interactions of different groups of people. Because of this it comes to possible to study the user‟s opinion. Two popular topics in the study of social networks are community detection and finding trends. Trend Analysis is an ongoing field of research in Data mining field, May referred as an “Opinion Mining” by some researchers. Trend Analysis is the computational treatment of opinions, sentiments and subjectivity of text. The related field to Trend Analysis contains finding emotions in a statement which attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Trend Analysis along with the community detection and their related fields with brief details. The main contributions of this paper include the overall analysis to bridge the concept between trend analyses with community detection. To find out the anomaly detection in a community with using the concept of trend analysis is a difficult task.


Question: Sentiment analysis of financial microblogs for bearish and bullish trends.

A novel methodology is introduced in this study for forecasting intraday movements of currency pairs in the foreign exchange market through the analysis of real-time financial news headlines. The rationale for this investigation is twofold: firstly, to refine and advance text-mining techniques employed for market prediction, addressing their current limitations, particularly the high dimensionality of the data and the lack of consideration for sentiment and semantics in text analysis. This research posits that improving these aspects of text-mining can enhance the predictive accuracy. The proposed system validates this hypothesis, demonstrating improved outcomes. Secondly, the study focuses on the foreign exchange market, a domain less explored in previous predictive text-mining research. The results of this study confirm a predictive correlation between currency market movements and financial news text. Additional unique features of the proposed system include the exclusive use of news headlines rather than full articles, facilitating the analysis of concise textual data, and the application of general financial news without additional filtering. To achieve these objectives, a multi-layered algorithm is developed to address each text-mining challenge at a specific level. The Semantic Abstraction Layer targets co-reference issues, where multiple words refer to the same concept, by introducing a heuristic-based feature selection method, Heuristic-Hypernyms, to group synonymous terms, thus reducing feature space noise and enhancing prediction accuracy. The Sentiment Integration Layer incorporates sentiment analysis by assigning a sentiment weight, SumScore, reflecting investor sentiment, and prunes dimensions lacking sentiment value to improve predictive accuracy. The Dynamic Model Creation Layer, Synchronous Targeted Feature Reduction (STFR), is designed for text stream mining, updating models with the latest data and minimizing dimensions for optimal performance. The algorithm and its layers are rigorously evaluated with real-world market data and news content over several years, outperforming comparable solutions. The system achieves exceptional directional accuracy, up to 83.33%, and provides a robust research framework and scalable, modular design for future enhancements. This paper offers detailed instructions for replicating the system and experiments, contributing significantly to the field of financial text analysis.

Twitter is one of world most famous social media. There are many statement expresed in Twitter like happiness, sadness, public information, etc. Unfortunately, people may got angry to each other and write it down as a tweet on Twitter. Some tweet may contain Indonesian swear words. It's serious problem because many Indonesians may not tolerated swear words. Some Indonesian swear words may have multiple means, not always an Indonesian swear word means insulting. Twitter has provide tweet's data by account, trending topics, and advance keyword. This work try to analyze many tweet about political news, political event, and some Indonesian famous person because the tweet assumed contains many Indonesian swear word. The derived tweets will process in text mining and then analyzed by classification process using Naive Bayes Classifier, Nearest Neighbor, and Decision Tree to detect Indonesian swear word. This work expected to discover the high accurate classification model. It means, the model can differentiate the real meaning of Indonesian swear word contained in tweet.

Sentiment analysis is a challenging task that attracted increasing interest during the last years. The availability of online data along with the business interest to keep up with consumer feedback generates a constant demand for online analysis of user-generated content. A key role to this task plays the utilization of domain-specific lexicons of opinion words that enables algorithms to classify short snippets of text into sentiment classes (positive, negative). This process is known as dictionary-based sentiment analysis. The related work tends to solve this lexicon identification problem by either exploiting a corpus and a thesaurus or by manually defining a set of patterns that will extract opinion words. In this work, we propose an unsupervised approach for discovering patterns that will extract domain-specific dictionary. Our approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics. The outcome is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task. Experiments on user reviews coming from a diverse set of products demonstrate the utility of the proposed method. An implementation of the proposed approach in an easy to use application for extracting opinion words from any domain and evaluate their quality is also presented.

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.

Opinion Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, applications, long and short future research areas, Research gaps and future challenges in opinion mining. Further, an attempt has been made to discuss in detail the use of supervised, unsupervised, machine learning and case based reasoning techniques in opinion mining to perform computational treatment of sentiments.

Opinion miningrefers to the use of natural language processing, text analysis and computational linguistics to identify and extract the subjective information. Opinion Mining has become an indispensible part of online reviews which is in the present scenario. In the field of information retrieval, a various kinds of probabilistic topic modeling techniques have been used to analyze contents present in a document. A topic model is a generative technique for document. All

We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available1 for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.