求解柔性作业车间调度问题的两阶段参数自适应蚁群算法  

A Two-stage Parameter Adaptive Ant Colony Algorithm for Flexible Job Shop Scheduling Problem

作　　者：凌海峰[1] 王西山[1] 

机构地区：[1]合肥工业大学过程优化与智能决策教育部重点实验室,合肥230009

出　　处：《中国机械工程》2013年第24期3380-3385,共6页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(71071047);安徽省自然科学基金资助项目(1208085MG120)

摘　　要：针对柔性作业车间调度问题,提出了一种新的两阶段蚁群算法求解方案。在算法前期,采用细菌觅食趋化聚类技术判断蚁群所处的状态,自适应调整蚁群算法的参数,使算法快速收敛到全局最优解附近;在算法后期,利用混沌的随机性和遍历性特点来调整参数,有利于算法跳出局部最优。实验结果验证了该两阶段法的有效性。A new two--stage ant colony algorithm was proposed to solve the flexible job shop scheduling problem. At the early stage of the algorithm, bacterial foraging chemotaxis based cluste- ring technology was used to determine the state of ant colony,and the parameters of ant colony algo- rithm were adjusted adaptively to make the algorithm rapidly convergence to the nearly global optimal solution. At the late stage, the parameters were tuned based on the randomness and ergodicity of cha- os, beneficial to jump out of local optima. Experimental results verify the effectiveness of the two stage method.

关 键 词：柔性作业车间调度 蚁群算法 细菌觅食聚类算法 混沌 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置] TP301[自动化与计算机技术—控制科学与工程]