| 异步随机微粒群算法 |
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| 引用本文： | 陈保娣,曾建潮.异步随机微粒群算法[J].太原重型机械学院学报,2009(5):359-363. |
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| 作者姓名： | 陈保娣 曾建潮 |
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| 作者单位： | 太原科技大学系统仿真与计算机应用研究所,太原030024 |
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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。
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| 关 键 词： | 微粒群算法 随机微粒群算法 异步模式 局部搜索 |
Asynchronous Stochastic Particle Swarm Optimization CHEN Bao-di,ZENG Jian-chao |
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| CHEN Bao-di,ZENG Jian-chao.Asynchronous Stochastic Particle Swarm Optimization CHEN Bao-di,ZENG Jian-chao[J].Journal of Taiyuan Heavy Machinery Institute,2009(5):359-363. |
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| Authors: | CHEN Bao-di ZENG Jian-chao |
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| Affiliation: | ( Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Taiyuan 030024, China) |
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| Abstract: | 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. |
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| Keywords: | particle swarm optimization stochastic particle swarm optimization asynchronous pattern local search |
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