| 微粒群算法的研究现状与展望 |
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| 引用本文： | 王万良,唐宇.微粒群算法的研究现状与展望[J].浙江工业大学学报,2007,35(2):136-141. |
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| 作者姓名： | 王万良 唐宇 |
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| 作者单位： | 浙江工业大学,信息工程学院,浙江,杭州,310032 |
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| 摘 要： | 微粒群算法(PSO)是继蚁群算法提出之后的又一种新的进化计算技术.介绍了微粒群算法的产生背景,基本算法,算法流程,算法参数及其对算法性能的影响.围绕微粒群算法的改进形式,算法的应用等方面对微粒群算法的研究现状进行全面综述,其中特别提到了算法在生产调度领域的研究现状.最后就PSO算法进一步的研究工作进行了探讨和展望.
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| 关 键 词： | 群体智能 微粒群算法 生产调度 |
| 文章编号： | 1006-4303(2007)02-0136-06 |
| 修稿时间： | 2006-09-19 |
The state of art in particle swarm optimization algorithms |
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| WANG Wan-liang,TANG Yu.The state of art in particle swarm optimization algorithms[J].Journal of Zhejiang University of Technology,2007,35(2):136-141. |
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| Authors: | WANG Wan-liang TANG Yu |
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| Affiliation: | College of Information Engineering, Zhejiang University of Technology, Hangzhou 310032, China |
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| Abstract: | Particle swarm optimization(PSO) is a kind of novel evolution algorithm after ant colony algorithm.This paper systematically introduces and analyzes the background,the principle,process and parameters of particle swarm optimization(PSO) algorithm,and their influence on optimization performance of PSO.The state of art in PSO algorithm is comprehensively summarized in such aspects as improvements and applications of the algorithms.Especially,the research works in the domain of production scheduling problems are introduced.Finally,further research issues and some suggestions in future are discussed. |
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| Keywords: | swarm intelligence particle swarm optimization(PSO) production scheduling |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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