混合粒子群算法在柔性工作车间调度中的应用  

Hybrid Particle Swarm Optimization for Flexible Job-Shop Scheduling

作　　者：贾兆红[1] 陈华平[1] 孙耀晖[1] 

机构地区：[1]中国科学技术大学信息管理与决策科学系

出　　处：《系统仿真学报》2007年第20期4743-4747,共5页Journal of System Simulation

基　　金：国家自然科学基金(70671096);安徽省自然科学基金项目(050460404);中国科学技术大学研究生创新基金(KD2006059)

摘　　要：混沌是一种新颖的优化技术,具有随机性、遍历性的特点和易跳出局部极值的能力。为了提高粒子群优化算法(PSO)的性能,在PSO中引入混沌,优势互补,提出了一种混合PSO算法,并应用于柔性工作车间调度问题的求解。首先基于混沌对PSO的参数进行自适应优化,实现全局搜索与局部搜索间的有效平衡;然后,在PSO的搜索过程中引入混沌局部搜索策略,来提高解的精度和收敛速度。实验比较结果验证了该算法的全局搜索性能。As a new optimization technique, chaos bears randomicity, ergodicity and the superiority of escaping from a local optimum. By integrating the advantage of Chaos and PSO, a hybrid particle swarm optimization （FIPSO） algorithm was proposed and applied to solving the flexible job-shop scheduling problem （FJSP）. Parameters of PSO were adaptively chaotic optimized to efficiently balance the exploration and exploitation abilities. During the search process of PSO, the chaotic local optimizer was introduced to raise its resulting precision and convergence rate. The global search performance of HPSO was validated by the results of the comparative experiments.

关 键 词：混沌 粒子群优化 柔性工作车间调度 遗传算法(GA) 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]