基于混沌粒子群算法的多目标调度优化研究  

Research on multi-objective scheduling optimization based on Chaotic Particle Swarm Optimization Algorithm

作　　者：张琪[1,2] 屈卫清[1,3] 熊伟清[3] 

机构地区：[1]宁波大红鹰学院,浙江宁波315175 [2]西安交通大学,西安710000 [3]宁波大学,宁波315175

出　　处：《激光杂志》2015年第1期122-127,共6页Laser Journal

基　　金：宁波市自然科学基金2012A610071

摘　　要：针对当前车间调度多目标优化研究存在收敛速度慢、精度低的问题,提出了混沌多目标粒子群优化算法。在算法中,设计了一种新的叠加Logistic扰动的Tent混沌映射算子,通过该算子周期性地更新种群以保证种群的多样性;对收缩粒子群算法进行了扩展使其能够快速收敛到Pareto前沿。通过标准测试问题与实际应用对所提方法进行了验证,实验结果显示混沌多目标粒子群优化算法无论在收敛速度还是在优化精度上都优于其它典型多目标进化算法。Since the current job shop scheduling multi-objective optimization has the drawbacks of slow conver-gence speed and low accuracy, it proposes a chaotic multi-objective particle swarm optimization algorithm. In the algo-rithm, designed the Tent chaotic mapping a new stack Logistic disturbance, the operator periodically update population in order to ensure the diversity of population;on the contraction of particle swarm algorithm is extended so that it can rapidly converge to the Pareto front. The standard test problems and practical application to verify the proposed meth-od, experimental results show that the chaotic multi-objective particle swarm optimization algorithm both in conver-gence speed and optimization accuracy is better than other typical multi-objective evolutionary algorithm.

关 键 词：车间调度 混沌算子 种群多样性 多目标优化 粒子群算法 

分 类 号：TN911.7[电子电信—通信与信息系统]