基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题

doi:10.3724/SP.J.1087.2012.01932

计算机应用 ›› 2012, Vol. 32 ›› Issue (07): 1932-1934.DOI: 10.3724/SP.J.1087.2012.01932

基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题

李莉

东北林业大学信息与计算机工程学院，哈尔滨150040

收稿日期:2012-01-11 修回日期:2012-02-29 发布日期:2012-07-05 出版日期:2012-07-01
通讯作者: 李莉
作者简介:李莉(1977-),女,黑龙江哈尔滨人,讲师，博士,CCF会员，主要研究方向：企业智能计算、软件工程。
基金资助:
国家自然科学基金资助项目（60774041）;中央高校基本科研业务费专项(DL12BB08; DL10AB02)

Particle swarm optimization algorithm with self-adaptive parameter and chaos search for solving flexible job shop scheduling problem

LI Li

College of Information and Computer Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China

Received:2012-01-11 Revised:2012-02-29 Online:2012-07-05 Published:2012-07-01
Contact: LI Li

摘要/Abstract

摘要： 针对传统粒子群优化(PSO)算法在求解柔性作业车间调度问题中的不足，提出了基于自适应参数与混沌搜索的粒子群优化算法。对粒子群算法中的惯性系数等参数采用基于迭代搜索而自适应调整的方式，使粒子在初期以较大惯性进行大范围搜索，后期逐渐减小惯性而转入精细搜索。这种方法改变了传统粒子群算法在求解过程中的盲目随机与求解精度不高的问题；同时，通过在局部搜索过程中引入混沌技术，扩大对最优解的寻找范围，以此避免算法陷入局部最优，有效提高算法的全局寻优能力。实验结果表明，基于自适应参数与混沌搜索的粒子群优化算法在求解柔性作业车间调度问题(FJSP)时能够获得更优粒子适应度平均值及更好的优化目标。所提算法对求解柔性作业车间调度问题可行，有效。

关键词: 柔性作业车间调度, 自适应, 混沌搜索, 粒子群优化算法

Abstract: According to the shortcomings of traditional Particle Swarm Optimization (PSO) algorithm in solving flexible job-shop scheduling problem, this paper proposed particle swarm optimization algorithm with self-adaptive parameters and chaos search. According to the self-adaptive method of parameters such as inertia coefficient based on iteration, particles searched in large scale with high inertia at early time and then got into fine search by reducing inertia. This method solved the problem of blind randomness and low accuracy in traditional particle swarm algorithm. Introduction of chaos technology into local search could expand the search scale for optimization solution and reduce the possibility of falling into local extremum. This method could effectively improve global optimization ability of the algorithm. The experimental results show that the particle swarm optimization algorithm with self-adaptive parameters and chaos search can get more optimal average particle fitness and better optimization objective. The proposed algorithm is feasible and effective for Flexible Job-shop Scheduling Problem (FJSP).

Key words: flexible job shop scheduling, self-adaptive, chaos search, Particle Swarm Optimization (PSO) algorithm

中图分类号:

TP301

李莉. 基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题[J]. 计算机应用, 2012, 32(07): 1932-1934.

LI Li. Particle swarm optimization algorithm with self-adaptive parameter and chaos search for solving flexible job shop scheduling problem[J]. Journal of Computer Applications, 2012, 32(07): 1932-1934.

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基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题

Particle swarm optimization algorithm with self-adaptive parameter and chaos search for solving flexible job shop scheduling problem

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