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Optimal radial distribution system reconfiguration using fuzzy adaptation of evolutionary programming

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2003年第10期25卷 775-780页

作者： Venkatesh, B Ranjan, R Multimedia Univ Fac Engn & Technol Bukit Beruang 75450 Melaka Malaysia

Optimal reconfiguration of Radial Distribution System (RDS) is done under the umbrella of Supervisory Control and Data Acquisition (SCADA) systems to achieve the best voltage profile and minimal kW losses amongst several objectives. This problem requires the determination of the best combination of feeders from each loop in the RDS to be switched out such that the resulting RDS gives the optimal performance in the chosen circumstance. The problem has a discontinuous solution space and certain problem variables assume discrete values of zero or one. This paper proposes a method that uses fuzzy adaptation of evolutionary programming (FEP) as a solution technique. FEP technique has been chosen as it is particularly suited while solving optimization problems with discontinuous solution space and when the global optimum is desired. Fuzzy adaptation of EP is necessitated while considering optimization of multiple objectives. The proposed method is tested on established RDS and results are presented. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： radial distribution systems evolutionary programming tie-switches

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Distribution system planning with evolutionary programming and a reliability cost model

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 2000年第6期147卷 336-341页

作者： Lin, WM Yang, CD Tsay, MT Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan Cheng Shiu Inst Technol Dept Elect Engn Kaohsiung Taiwan

Cost of reliability of optimal distribution system planning is considered. The reliability cost model has been derived as a linear function of line no flows for evaluating the outages. The objective is to minimise the total cost including the outage cost, feeder resistive loss, and fixed investment cost. evolutionary programming was used to solve the very complicated mixed-integer, highly nonlinear and nondifferential problem. A real distribution network was modelled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process.

关键词： feeder resistive loss power distribution reliability Reliability optimal distribution system planning Optimisation techniques Optimisation highly nonlinear problem evolutionary programming total cost minimisation evolutionary computation reliability cost model nondifferential problem Distribution networks mixed-integer problem fixed investment cost distribution system planning outages evaluation Maintenance and reliability Power system planning and layout linear function power distribution planning line flows

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Adaptive evolutionary programming with p-best mutation strategy

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SWARM AND evolutionary COMPUTATION 2013年 9卷 58-68页

作者： Das, Swagatam Mallipeddi, Rammohan Maity, Dipankar Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Although initially conceived for evolving finite state machines, evolutionary programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization. EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we propose a p-best mutation scheme for EP where any one from the p (p is an element of [1,2, ... ,mu], where mu denotes population size) top-ranked population-members (according to fitness values) is selected randomly for mutation. The scheme is invoked with 50% probability with each index in the current population, i.e. the i-th offspring can now be obtained either by mutating the i-th parent or by mutating a randomly selected individual from the p top-ranked vectors. The percentage of best members is made dynamic by decreasing p in from mu/2 to 1 with generations to favor explorative behavior at the early stages of search and exploitation during the later stages. We investigate the effectiveness of introducing controlled bias in parent selection in conjunction with an Adaptive Fast EP (AFEP), where the value of a strategy parameter is updated based on the previous records of successful mutations by the same parameter. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on evolutionary Computation) 2005 test-suite for real-parameter optimization and two other engineering optimization problems reflects the statistically validated superiority of the new scheme in terms of final accuracy, speed, and robustness. Comparison with AFEP without p-best mutation demonstrates the imp

关键词： evolutionary programming Real-parameter optimization Mutation Parent selection evolutionary algorithms

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Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第4期33卷 939-946页

作者： Rajan, C. Christober Asir Pondicherry Engn Coll Dept EEE Pondicherry 605014 India

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded evolutionary programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Hydro-thermal unit commitment evolutionary programming Simulated annealing

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Multiuser detection ford DS-CDMA systems using evolutionary programming

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IEEE COMMUNICATIONS LETTERS 2003年第3期7卷 101-103页

作者： Lim, HS Rao, MVC Tan, AWC Chuah, HI Multimedia Univ Fac Engn & Technol Jalan Ayer Keroh Lama 75450 Melaka Malaysia

This letter introduces a new multiuser detection scheme which uses evolutionary programming (EP) to detect the user bits based on the maximum-likelihood decision rule. The major advantage of the proposed detector is that it has a lower computational complexity compared to other popular evolutionary-algorithm-based detectors. The simulation results show that the EP has always converged to the optimum solution with a small number of generations. The simulated average computational time performance demonstrates that this approach achieves practical ML performance with polynomial complexity in the number of users.

关键词： code-division multiple access (CDMA) evolutionary programming multiuser detection

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Solving Unit Commitment Problem Using Multi-agent evolutionary programming Incorporating Priority List

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015年第11期40卷 3247-3261页

作者： Othman, M. N. C. Rahman, T. K. A. Mokhlis, H. Aman, M. M. Univ Tenaga Nas Dept Elect Power Engn Coll Engn Kajang 43000 Selangor Malaysia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21413 Saudi Arabia Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur 50603 Malaysia

This paper presents an approach to solve the unit commitment problem using a newly developed Multi-agent evolutionary programming incorporating Priority List optimisation technique (MAEP-PL). The objective of this study is to search for generation scheduling such that the total operating cost can be minimised when subjected to a variety of constraints, while at the same time reducing its computational time. The proposed technique assimilates the concepts of Priority Listing (PL), Multi-agent System (MAS) and evolutionary programming (EP) as its basis. In the proposed technique, deterministic PL technique is applied to produce a population of initial solutions. The search process is refined using heuristic EP-based algorithm with multi-agent approach to produce the final solution. The developed technique is tested on ten generating units test system for a 24-h scheduling period, and the results are compared with the standard evolutionary programming (EP), evolutionary programming with Priority Listing (EP-PL) and Multi-agent evolutionary programming (MAEP) optimisation techniques. From the obtained results and the comparative studies, it was found that the proposed MAEP-PL optimisation technique is able to solve the unit commitment problem where the total daily generation cost is effectively minimised and the computation time is reduced as compared to other techniques.

关键词： Unit commitment Multi-agent evolutionary programming Priority list

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An efficient evolutionary programming algorithm

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COMPUTERS & OPERATIONS RESEARCH 1999年第7期26卷 645-663页

作者： Zhang, JH Xu, XH Northeastern Univ Control & Simulat Ctr Shenyang 110006 Peoples R China

A novel evolutionary programming algorithm, which not only has a rapid convergence rate but also maintains the diversity of the population so as to escape from local optima, is proposed in this paper. In addition, a multi-modal test function is presented and is used to indicate the efficiency of this algorithm. Several application examples are given to show its usefulness. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： combinatorial optimization evolutionary programming reliability optimization

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A clustering algorithm using an evolutionary programming-based approach

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PATTERN RECOGNITION LETTERS 1997年第10期18卷 975-986页

作者： Sarkar, M Yegnanarayana, B Khemani, D Indian Inst Technol Dept Comp Sci & Engn Madras 600036 Tamil Nadu India

In this paper, an evolutionary programming-based clustering algorithm is proposed. The algorithm effectively groups a given set of data into an optimum number of clusters. The proposed method is applicable for clustering tasks where clusters are crisp and spherical. This algorithm determines the number of clusters and the cluster centers in such a way that locally optimal solutions are avoided. The result of the algorithm does not depend critically on the choice of the initial cluster centers. (C) 1997 Published by Elsevier Science B.V.

关键词： clustering K-means optimization evolutionary programming

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Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints

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IEEE TRANSACTIONS ON POWER SYSTEMS 2003年第2期18卷 688-697页

作者： Venkatesh, P Gnanadass, R Padhy, NP Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Indian Inst Technol Dept Elect Engn Roorkee 247667 Uttar Pradesh India

Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuelcost and optimal emission of generating units, respectively. Combined economic emission dispatch (CEED) problem is obtained by considering both the economy and emission objectives. This biobjective CEED problem is converted into a single objective function using a price penalty factor approach. A novel modified price penalty factor is proposed to solve the CEED problem. In this paper, evolutionary computation (EC) methods such as genetic algorithm (GA), micro GA, (NIGA), and evolutionary programming (EP) are applied to obtain ELD solutions for three-, six-, and 13-unit systems. Investigations showed that EP? was better arnong EC methods in solving the ELD problem. EP-based CEED. problem has been tested on IEEE 14-, 30-, and 118-bus systems with and without line flow constraints. A nonlinear scaling factor is also included in EP algorithm to improve the convergence performance for the 13 units and IEEE test systems. The solutions obtained are quite encouraging and useful in the economic emission environment.

关键词： combined cycle cogeneration plant combined economic emission dispatch economic load dispatch evolutionary programming price penalty factor

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One-dimensional evolutionary programming-based CLEAN

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ELECTRONICS LETTERS 2001年第6期37卷 400-401页

作者： Choi, IS Kim, HT Pohang Univ Sci & Technol Dept Elect & Elect Engn Nam Gu Pohang 790784 Kyungbuk South Korea

A novel one-dimensional scattering centre extraction method using an evolutionary programming and undamped exponential model is proposed. The method is robust and fast. Moreover. no resolution problems appeared in FFT-based CLEAN. Experimental results show that the proposed algorithm can be successfully applied to one-dimensional scattering centre extraction.

关键词： fast Fourier transforms ideal point scatterers backscattered signals Integral transforms in numerical analysis Optimisation techniques evolutionary computation radar signal processing undamped exponential model radar target recognition backscatter one-dimensional scattering centre extraction method Signal processing and detection FFT-based CLEAN Radar theory evolutionary programming

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