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IEEE-Power-Engineering-Society General Meeting

作者： Abbasy, Alireza Hosseini, Seyed Hanlid Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781424412969

This paper presents a new approach to economic dispatch (ED) problem with non-continuous and non-smooth cost functions using a hybrid evolutionary programming (EP) algorithm. In the proposed method the concept of multi-agent (MA) systems and EP are integrated together to form a new multi-agent evolutionary programming (MAEP) approach. In MAEP, an agent represents a candidate solution to the optimization problem in hand, and all agents live together in a global environment. Each agent senses its local environment, competes with its neighbors, and also learns by using its own knowledge. MAEP uses these agent-agent interactions and the evolutionary mechanism of EP to obtain the optimal solution. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms in the literature.

关键词： economic dispatch evolutionary programming multi-agent systems

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Application of multi objective evolutionary programming to combined economic emission dispatch problem

Application of multi objective evolutionary programming to c...

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International Joint Conference on Neural Networks

作者： Jeyakumar, D. N. Venkatesh, P. Lee, Kwang. Y. PSNA Coll Engn Dept Elect & Elect Engn Dindigul India Univ Penn Dept Elect Engn University Pk PA 16802 USA

ISBN: (纸本)9781424413799

This paper describes a new Multi-Objective evolutionary programming (MOEP) method to solve the Combined Economic Emission Dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using weighted sum method. Hence the MOEP is proposed by employing the non-dominated solution ranking as selection mechanism for the bio-objective CEED problem. The developed algorithm is tested for a three-unit and a six-unit system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective CEED problem in a single run.

关键词： combined economic emission dispatch evolutionary programming non-dominated solution Pareto optimal solutions.

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Use of the q-Gaussian mutation in evolutionary algorithms

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SOFT COMPUTING 2011年第8期15卷 1523-1549页

作者： Tinos, Renato Yang, Shengxiang Univ Sao Paulo Dept Math & Phys FFCLRP BR-14040901 Ribeirao Preto SP Brazil Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.

关键词： evolutionary algorithms q-Gaussian distribution Self-adaptation evolutionary programming Mutation distribution

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Constraint-handling in nature-inspired numerical optimization: Past, present and future

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SWARM AND evolutionary COMPUTATION 2011年第4期1卷 173-194页

作者： Mezura-Montes, Efren Coello Coello, Carlos A. Lab Nacl Informat Avanzada LANIA AC Rebsamen 80 Xalapa 91000 Veracruz Mexico Ctr Invest & Estudios Avanzados IPN CINVESTAV Dept Computac Evolutionary Computat Grp Mexicali 07360 Baja California Mexico

In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Constraint-handling Differential evolution Evolution strategies Particle swarm optimization Genetic algorithms evolutionary programming

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An improved differential evolution algorithm with fitness-based adaptation of the control parameters

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INFORMATION SCIENCES 2011年第18期181卷 3749-3765页

作者： Ghosh, Arnob Das, Swagatam Chowdhury, Aritra Gini, Ritwik Jadavpur Univ Dept Elect & Telecomm Engg Kolkata 700032 India

Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through the similar computational steps as employed by a standard evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Scale Factor (F) and Crossover Rate (Cr) are two very important control parameters of DE since the former regulates the step-size taken while mutating a population member in DE and the latter controls the number of search variables inherited by an offspring from its parent during recombination. This article describes a very simple yet very much effective adaptation technique for tuning both F and Cr, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in the DE population. Comparison with the best-known and expensive variants of DE over fourteen well-known numerical benchmarks and one real-life engineering problem reflects the superiority of proposed parameter tuning scheme in terms of accuracy, convergence speed, and robustness. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Numerical optimization Differential evolution Genetic algorithms evolutionary programming Evolution strategies Parameter tuning

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A new EP-based α-β-γ-δ filter for target tracking

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MATHEMATICS AND COMPUTERS IN SIMULATION 2011年第9期81卷 1785-1794页

作者： Wu, Chun-Mu Chang, Ching-Kao Chu, Tung-Te Kao Yuan Univ Dept Mech Engn & Automat Engn Lujhu Township 821 Kaohsiung Cty Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 811 Taiwan

This study explores a new fourth-order target-tracking alpha-beta-gamma-delta filter using an evolutionary programming (EP) for numerical simulation in view that the current third-order-alpha-beta-gamma filter system tracks only the target's position and velocity but not its acceleration. As demonstrated, the new alpha-beta-gamma-delta filter exhibits a significantly improved tracking accuracy over the conventional alpha-beta-gamma filter. Not unexpectedly, however, the new alpha-beta-gamma-delta filter takes more computation time in the optimization process. To overcome this weakness, an optimal simulation technique via EP is proposed. The developed EP-based alpha-beta-gamma-delta filter finds not only the optimal set of filter parameters to minimize position tracking errors but could also reduce the computation time by up to 95% in some time steps. The trajectory simulated by the EP-based alpha-beta-gamma-delta filter is compared with those by other filters to illustrate the efficiency of the former filter. (C) 2011 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Target tracker evolutionary programming The alpha-beta-gamma-delta filter The alpha-beta-gamma filter The EP-based alpha-beta-gamma-delta filter

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Optimal digital tracker for analog neutral multiple time-delay systems via evolutionary programming and high gain approach

Optimal digital tracker for analog neutral multiple time-del...

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IEEE International Conference on Control and Automation

作者： Tsai, Jason S. H. Tsai, Tzong-Jiy Guo, Shu-Mei Natl Cheng Kung Univ Control Syst Lab Dept Elect Engn Tainan 701 Taiwan Tung Fang Inst Technol Automat Control Lab Dept Elect Engn Kaohsiung Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Tainan Taiwan

ISBN: (纸本)9781424408177

The optimal hybrid tracking control problem for analog neutral systems with multiple discrete and distributed time delays is discussed in this paper. In order to obtain good tracking performance and improve the drawback of conventional optimal control in selecting the weighting matrices, the observer with evolutionary-programming (EP)-based alternative digital redesign control technique is presented to find a low-gain digital tracker for hybrid control of the analog neutral system. A novel approach that combining the EP method and the high-gain property is proposed to search the optimal weighting matrices in the performance index to achieve the "best" tracking control for analog neutral systems for the first time. Finally, a numerical example is given to illustrate the proposed methods.

关键词： optimal control neutral system evolutionary programming digital redesign observer

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Optimal power flow for load margin improvement using evolutionary programming

Optimal power flow for load margin improvement using evoluti...

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5th Student Conference on Research and Development

作者： Aminudin, Norziana Rahman, Titik Khawa Abdul Musirin, Ismail Univ Teknol MARA Shah Alam Malaysia

ISBN: (纸本)9781424414697

Optimal power flow (OPF) has been profoundly identified as one of the main issues in power system operation and planning. Solving the OPF problem is the fundamental aspect in the determination of electricity prices and congestion management. It involves a large dimension nonlinear, non-convex and highly constrained optimization problem which requires a reliable technique in order to provide global optimal solution. This paper presents the application of evolutionary programming (EP) technique in OPF for load margin enhancement with consideration of operational cost and loss reduction. In realizing the effectiveness of the proposed technique, validation was conducted on the IEEE-26 reliability test system. The results obtained from the pre-optimization and post-optimization are compared. The results revealed that the proposed OPF using EP has successfully improved the load margin taking the pre-optimization results as reference.

关键词： optimal power flow evolutionary programming load margin maximum loading point

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Integrating individual search and navigation behaviors in mechanistic movement models

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THEORETICAL ECOLOGY 2011年第3期4卷 341-355页

作者： Mueller, Thomas Fagan, William F. Grimm, Volker Univ Maryland Dept Biol College Pk MD 20742 USA Conservat Ecol Ctr Front Royal VA 22630 USA UFZ Helmholtz Ctr Environm Res Dept Ecol Modelling D-04318 Leipzig Germany

Understanding complex movement behaviors via mechanistic models is one key challenge in movement ecology. We built a theoretical simulation model using evolutionarily trained artificial neural networks (ANNs) wherein individuals evolve movement behaviors in response to resource landscapes on which they search and navigate. We distinguished among non-oriented movements in response to proximate stimuli, oriented movements utilizing perceptual cues from distant targets, and memory mechanisms that assume prior knowledge of a target's location and then tested the relevance of these three movement behaviors in relation to size of resource patches, predictability of resource landscapes, and the occurrence of movement barriers. Individuals were more efficient in locating resources under larger patch sizes and predictable landscapes when memory was advantageous. However, memory was also frequently used in unpredictable landscapes with intermediate patch sizes to systematically search the entire spatial domain, and because of this, we suggest that memory may be important in explaining super-diffusion observed in many empirical studies. The sudden imposition of movement barriers had the greatest effect under predictable landscapes and temporarily eliminated the benefits of memory. Overall, we demonstrate how movement behaviors that are linked to certain cognitive abilities can be represented by state variables in ANNs and how, by altering these state variables, the relevance of different behaviors under different spatiotemporal resource dynamics can be tested. If adapted to fit empirical movement paths, methods described here could help reveal behavioral mechanisms of real animals and predict effects of anthropogenic landscape changes on animal movement.

关键词： Individual-based models evolutionary programming Emergent behavior Animal movement Search Navigation

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Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2011年第2期2卷 151-169页

作者： Ibanez, Eduardo McCalley, James D. Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

The transportation and electric sectors are by far the largest producers of greenhouse emissions in the United States while they consume a significant amount of the national energy. The ever rising demand for these systems, the growing public concern on issues like global warming or national security, along with emerging technologies that promise great synergies between both (plug-in hybrid vehicles or electrified rail), creates the necessity for a new framework for long-term planning. This paper presents a comprehensive methodology to investigate long-term investment portfolios of these two infrastructures and their interdependencies. Its multiobjective nature, based on the NSGA-II evolutionary algorithm, assures the discovery of the Pareto front of solutions in terms of cost, sustainability and resiliency. The optimization is driven by a cost-minimization network flow program which is modified in order to explore the solution space. The modular design enables the use of metrics to evaluate sustainability and resiliency and better characterize the objectives that the systems must meet. An index is presented to robustly meet long-term emission reduction goals. An example of a high level representation of the continental United States through 2050 is presented and analyzed using the present methodology.

关键词： Energy Transportation Long-term investment Multiobjective optimization evolutionary programming

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