文章基本信息

标题：An Improved Adaptive Dynamic Particle Swarm Optimization Algorithm
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作者：Zhao, Hongbo ; Feng, Lina
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2014
卷号：9
期号：2
页码：488-494
DOI：10.4304/jnw.9.2.488-494
语种：English
出版社：Academy Publisher
摘要：In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake of investigates the timing of taking effect of influence function. The influence function is adaptively adopted for the aberrance and renovation of the population space when algorithm is trapped into local optimization, so as to efficiently exert the mechanism of “double evolving and promoting” in cultural particle swarm optimization algorithm. According to the premature convergence degree of population space to adaptively adjust the inertia weight of particle, it makes the population can maintain diversity of the inertia weight during evolution and keep the balance between the global convergence property and the convergence speed. Finally, the test on four benchmark functions shows that the proposed algorithm not only has strong search capability, but also convergence speed and precision of the algorithm is improved effectively.
关键词：The Particle Swarm Optimization;Function Convergence Property;Optimization Algorithm