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| 双群体随机微粒群算法 | |
| 引用本文： | 夏桂梅,曾建潮. 双群体随机微粒群算法[J]. 计算机工程与应用, 2006, 42(24): 46-48 |
| 作者姓名： | 夏桂梅 曾建潮 |
| 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;太原科技大学系统仿真与计算机应用研究所,太原,030024 |
| 基金项目： | 教育部科学技术研究项目 |
| 摘 要： | 以保证全局收敛的随机微粒群算法为基础,文章提出了一种双群体随机微粒群算法——DB-SPSO。该方法采用两个群体同时进化,一个群体在进化过程中所出现的停止微粒由另一群体的微粒来代替,并和此群体中其余的微粒一起继续进化。通过对此算法的参数适用范围及收敛率进行讨论,给出了此算法的适用范围。其仿真结果表明:对于单峰函数和多峰函数,此算法都能够取得较好的优化效果。 |
| 关 键 词： | 随机微粒群算法 双群体 全局优化 |
| 文章编号： | 1002-8331-（2006）24-0046-03 |
| 收稿时间： | 2005-11-01 |
| 修稿时间： | 2005-11-01 |
A Stochastic Particle Swarm Optimization Algorithm Based on Double Particle Swarm |
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| Xia Guimei,Zeng Jianchao. A Stochastic Particle Swarm Optimization Algorithm Based on Double Particle Swarm[J]. Computer Engineering and Applications, 2006, 42(24): 46-48 | |
| Authors: | Xia Guimei Zeng Jianchao |
| Affiliation: | Division of System Simulation and Computer Application,Tianyuan University of Science and Technology,Taiyuan 030024 |
| Abstract: | Based on the stochastic particle swarm optimization algorithm that guarantees global convergence,an improved stochastic particle swarm optimization algorithm DB-SPSO is proposed.This method adopts two particle swarm putting up evolution at one time.During the evolution of SPSO,the particle produced by another particle swarm substitutes for the stopping particle and takes part in the evolution of next generation.And the results of computer simulation indicate that the DB-SPSO could get better performance in one-peak functions and multi-peak functions. |
| Keywords: | stochastic particle swarm optimization double particle swarm global optimization |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |