A Novel adaptive elite-based particle swarm optimization applied to VAR optimization in electric power systems

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/106397

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106397

Title:	A Novel adaptive elite-based particle swarm optimization applied to VAR optimization in electric power systems
Authors:	林法正;Hong, Ying-Yi;Lin, Faa-Jeng;Chen, Syuan-Yi;Lin, Yu-Chun;Hsu, Fu-Yuan
Contributors:	資訊電機學院電機工程學系
Keywords:	Adaptive systems;Atoms & subatomic particles;Cloning;Electric power systems;Electrical engineering;Evolutionary algorithms;Evolutionary computation;Genetic algorithms;Heuristic;Mathematical analysis;Mutation;Optimization;Optimization algorithms;Particle swarm optimization;Population;Searching;Solution space;Standard deviation;Strategy;Swarm intelligence
Date:	2014-01-01
Issue Date:	2026-04-23 13:20:07 (UTC+8)
Publisher:	Hindawi Publishing Corporation;New York: Hindawi Publishing Corporation
Abstract:	摘要： Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method. 出版者： New York: Hindawi Publishing Corporation 出版日期： 2014-01-01 出處： Mathematical problems in engineering, 2014-01, Vol.2014 (1) 資源來源：華藝CEPS中文電子期刊服務版權： Copyright © 2014 Ying-Yi Hong et al. 版權： Copyright © 2014 Ying-Yi Hong et al. Ying-Yi Hong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 識別號： ISSN: 1024-123X 識別號： ISSN: 1026-7077 識別號： ISSN: 1563-5147 識別號： EISSN: 1563-5147 識別號： DOI: 10.1155/2014/761403
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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