《计算机应用研究》|Application Research of Computers

改进的云自适应粒子群优化算法

Improved adaptive particle swarm optimization algorithm based on cloud theory

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作者 张艳琼
机构 南京特殊教育职业技术学院 信息系,南京 210038
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2010)09-3250-03
DOI 10.3969/j.issn.1001-3695.2010.09.012
摘要 为了提高基本PSO算法搜索性能和个体寻优能力,加快收敛速度,提出一种新的云自适应粒子群优化算法(CPSO)。此算法利用云滴具有随机性、稳定倾向性等特点,结合不同粒子与全局最优点的距离动态变化的性质,提出云自适应调整算法用于计算惯性权重,并对新算法进行了描述。通过典型函数优化实验表明,该算法较基本PSO明显提高了全局搜索能力和收敛速度,改善了优化性能。
关键词 粒子群优化;自适应参数调整;云模型;全局最优性
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英文标题 Improved adaptive particle swarm optimization algorithm based on cloud theory
作者英文名 ZHANG Yan-qiong
机构英文名 Dept. of Information, Nanjing Technical College of Special Education, Nanjing 210038, China
英文摘要 For the purpose of improving the basic PSO’s search performance and individual optimizing ability, speeding up the convergence, presented an adaptive particle swarm optimization based on cloud theory (CPSO), relative to the basic PSO algorithm. The inertia weight was adaptively varied depending on X-conditional cloud generator. The inertia weight had the stable tendency and randomness property because of the cloud model and the distance between the particle and the current optimal position. Experimental results show CPSO can greatly improve the global convergence ability and enhance the rate of convergence.
英文关键词 particle swarm optimization(PSO); adaptive parameter adjusting; cloud theory; global optimality
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页码 3250-3252
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文献标志码 A