Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization
作者: Quande QinShi ChengQingyu ZhangYiming WeiYuhui Shi
作者单位: 1Department of Management Science, Shenzhen University, Shenzhen, China
2Research Institute of Business Analytics and Supply Chain Management, Shenzhen, China
3School of Management and Economics, Beijing Institute of Technology, Beijing, China
4Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
5Division of Computer Science, The University of Nottingham Ningbo, China
6International Doctoral Innovation Centre, The University of Nottingham Ningbo, China
7Department of Electrical & Electronic Engineering, Xi׳an Jiaotong-Liverpool University, Suzhou, China
刊名: Computers and Operations Research, 2015, Vol.60 , pp.91-110
来源数据库: Elsevier Journal
DOI: 10.1016/j.cor.2015.02.008
关键词: Global optimizationLearning strategyOpposition-based learningOrthogonal designParticle swarm optimizationEconomic load dispatch problems
原始语种摘要: Abstract(#br)In the canonical particle swarm optimization (PSO), each particle updates its velocity and position by taking its historical best experience and its neighbors׳ best experience as exemplars and adding them together. Its performance is largely dependent on the employed exemplars. However, this learning strategy in the canonical PSO is inefficient when complex problems are being optimized. In this paper, Multiple Strategies based Orthogonal Design PSO (MSODPSO) is presented, in which the social-only model or the cognition-only model is utilized in each particle׳s velocity update, and an orthogonal design (OD) method is used with a small probability to construct a new exemplar in each iteration. In order to enhance the efficiency of OD method and obtain more efficient exemplar,...
全文获取路径: Elsevier  (合作)
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影响因子:1.909 (2012)

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