熵增强的混沌粒子群算法在车间调度中的应用  

Entropy-enhanced Particle Swarm Optimization with Chaos Search for Job Shop Scheduling Problems

作　　者：黄英杰 姚锡凡[2] 申辉阳[1] HUANG Ying-jie;YAO Xi-fan;SHEN Hui-yang(School of Electrical Engineering,GuangDong JiDian Polytechnic,Guangzhou 510515,China;School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]广东机电职业技术学院电气工程学院,广州510515 [2]华南理工大学机械与汽车工程学院,广州510640

出　　处：《组合机床与自动化加工技术》2018年第9期152-155,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(51675186;51175187)

摘　　要：粒子群算法具有早期收敛速度快,后期容易陷入早熟、局部最优等特点,为了使粒子群算法的择优能力大幅提升,论文首先选择运用混沌映射产生最初种群,然后借助粒子群算法针对种群展开优化,对个体及全局最优解加以混沌搜索,同时按照信息熵自适应调节惯性系数,设计出在大规模车间调度问题求解当中较为适用的熵增强的混沌粒子群算法。通过具有代表性的实际范例对该算法进行仿真研究,结果显示,在面对大规模的车间调度问题时采用该算法能够高效、快速获取相应答案,相较于以往老旧的算法,其优势极为显著。The particle swarm optimization has the advantages of fast convergence,but with the shortcoming of premature and local convergence.Optimizing the initial population by the particle swarm optimization which was generated by chaotic map,then optimizing the individual and global best solutions by Chaos search algorithm,and adjusting inertia factor and mutation based on information entropy in order to improve the searching ability of the particle swarm optimization,the paper proposed a hybrid particle swarm optimization for larger-scale job shop scheduling problems.and benchmark instances were used to verify the algorithm with simulation.Simulation results show that the proposed algorithm can well solve larger-scale job shop scheduling problems,and has obvious advantages over traditional scheduling algorithms.

关 键 词：粒子群算法 车间调度 混沌搜索算法 信息熵 

分 类 号：TH166[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]