基于云模型的PSO算法求解差异工件单机批调度问题  

Particle Swarm Optimization Based on Cloud Model for Scheduling Single Batch-Processing Machine with Non-Identical Job Sizes

作　　者：刘娟[1] 陈华平[1] 

机构地区：[1]中国科学技术大学管理学院,安徽合肥230026

出　　处：《计算机系统应用》2010年第2期164-168,共5页Computer Systems & Applications

基　　金：国家自然基金(70671096);国家杰出青年基金(B类)(70629002)

摘　　要：为了提高粒子群(PSO)算法的性能,提出一种基于云模型理论的改进PSO算法,并应用于差异工件单机批调度问题的求解。首先根据粒子的适应值把种群划分为三个子群,提出一种随机的位置和速度更新方法,来有效平衡算法的局部和全局搜索;然后引入基于云模型理论的自适应参数策略,不同的子群采用不同的惯性权重生成方法,提高种群的多样性和算法的收敛速度。实验比较结果验证了该算法的全局搜索性能。An adaptive Particle Swarm Optimization based on cloud model theory is proposed to improve its capability and applied to minimizing the makespan of a single batch-processing machine with non-identical job sizes. The particles are first divided into three groups based on the fitness of the particle to propose a new method for updating location and velocity. Then an adaptive strategy for varying parameters of PSO based on cloud model theory is introduced and different groups adopted different inertia weight generating methods, which does not only improve the convergence speed, but also maintain the diversity of the population. The global search performance of this adaptive algorithm is validated by the results of the comparative experiments.

关 键 词：云模型 粒子群优化 批调度 自适应参数调整 惯性权重 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] O224[自动化与计算机技术—计算机科学与技术]