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异步随机微粒群算法 被引量：2

Asynchronous Stochastic Particle Swarm Optimization
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摘要 在研究微粒群算法生物特征的基础上,提出了一种异步随机微粒群算法——ASPSO.该方法是在微粒的进化过程中,采用异步模式使全局最好位置信息以异步方式在种群中传播。从理论上证明了ASPSO与同步模式微粒群算法SPSO相比较具有更快的局部收敛速度,并对四个经典测试函数进行了仿真测试,测试结果表明:与SPSO相比,ASPSO算法具有更快的收敛速度。 Based on the biological characteristics of particle swarm optimization, an asynchronous stochastic particle swarm optimization（ASPSO） is proposed. In the evolution process of particles, using asynchronous pattern,the information of global best position can be asynchronously transmitted in the population. Then theoretical analysis has been made to prove that the local convergent rate of ASPSO is faster than the synchronous pattern algorithm SPSO. Moreover, the simulation tests of four classic functions have been done, and the test results show that：the ASPSO owns a faster convergence rate compared with the SPSO.
作者 陈保娣 曾建潮
机构地区 太原科技大学系统仿真与计算机应用研究所
出处 《太原科技大学学报》 2009年第5期359-363,共5页 Journal of Taiyuan University of Science and Technology
关键词 微粒群算法 随机微粒群算法 异步模式 局部搜索 particle swarm optimization, stochastic particle swarm optimization, asynchronous pattern,local search
分类号 P301.6 [天文地球—地球物理学]
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参考文献7

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共引文献157

同被引文献20

  • 1刘红星,肇莹,朱博,高敦堂.基于进化算法的优化平台设计[J].计算机工程与应用,2007,43(1):100-103. 被引量：3
  • 2龚薇华,王晨光,俞欢军.基于MVC模式和.NET的公司内部管理信息系统设计[J].计算机工程与设计,2007,28(9):2142-2144. 被引量：39
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太原科技大学学报
2009年 第5期

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