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随机选择最优个体的量子粒子群优化算法 被引量：4

Quantum-behaved particle swarm optimization algorithm with random selection of optimal individual
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摘要 在分析量子行为粒子群优化算法的基础上,针对算法后期粒子群体容易聚集到一个狭小搜索区域,群体多样性降低的问题,提出了在算法中引入随机选择最优个体的改进方法,提高算法搜索过程中粒子群体的多样性。将改进后的量子粒子群算法与量子粒子群算法、粒子群算法通过benchmark测试函数进行了比较,仿真结果表明改进后的算法更适合解决多峰类的优化问题。 The particles are easy to mass into a small search space in late stage and thus the diversity of swarms decline. Based on the analysis of quantum-behaved Particle Swarm Optimization （PSO） algorithm, a method of random selection of optimal individual was proposed to improve the diversity in searching progress. The of the improved QPSO was compared with the original PSO and the original QPSO using the testing function of the benchmark. Experimental results demonstrate that the improved QPSO is more suitable for resolving the multi-peak optimization problem.
作者 周阳花 黄麟 奚茂龙
机构地区 江南大学信息工程学院 无锡职业技术学院机电技术分院
出处 《计算机应用》 CSCD 北大核心 2009年第6期1554-1558,共5页 journal of Computer Applications
关键词 粒子群算法 量子行为 随机选择 最优个体 Particle Swarm Optimization （PSO） algorithm quantum behavior random selection optimal individual
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献12

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同被引文献16

  • 1李爱国.多粒子群协同优化算法[J].复旦学报（自然科学版）,2004,43(5):923-925. 被引量：398
  • 2夏桂梅,曾建潮.基于锦标赛选择遗传算法的随机微粒群算法[J].计算机工程与应用,2007,43(4):51-53. 被引量：17
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计算机应用
2009年 第6期

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