| An Extended Particle Swarm Optimizer |
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| 引用本文： | XU Jun-jie,XIN Zhan-hongSchool of Economics and Management,Beijing University of Posts and Telecommunications,Beijing 100876,P.R. China. An Extended Particle Swarm Optimizer[J]. 中国邮电高校学报(英文版), 2005, 12(3) |
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| 作者姓名： | XU Jun-jie XIN Zhan-hongSchool of Economics and Management Beijing University of Posts and Telecommunications Beijing 100876 P.R. China |
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| 作者单位： | XU Jun-jie，XIN Zhan-hongSchool of Economics and Management，Beijing University of Posts and Telecommunications，Beijing 100876，P.R. China |
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| 摘 要： | 1 Introduction Particle Swarm Opti mization (PSO)[1 ~3]is a novelevolutionary computation technique developed byKennedy and Eberhart . Through si mulating bird flock-ing,it has obtained success in opti mizing of continuousnonlinear functions . The main concept of this opti mizerliesin sharinginformation amongthe collaboratingindi-viduals. Paradigms of PSO are i mplemented in a fewlines of computer code andlowcomputation cost will berequired,so many research works about PSOhave beenpropos…
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An Extended Particle Swarm Optimizer |
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| XU Jun-jie,XIN Zhan-hong. An Extended Particle Swarm Optimizer[J]. The Journal of China Universities of Posts and Telecommunications, 2005, 12(3) |
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| Authors: | XU Jun-jie XIN Zhan-hong |
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| Abstract: | An Extended Particle Swarm Optimizer (EPSO) is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle's velocity updating process. EPSO is an integration of Local Best paradigm (LBEST) and Global Best paradigm (GBEST) and it significantly enhances the performance of the conventional particle swarm optimizers. The experiment results have proved that EPSO deserves to be investigated. |
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| Keywords: | particle swarm optimization simulating biology intelligent algorithm evolutionary computation |
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