文献类型：期刊文献

中文题名：基于GMOGSO的多目标流水车间调度问题

英文题名：Multi-objective flow shop scheduling problem based on GMOGSO

作者：徐震浩[1];李继明[1];顾幸生[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2016

卷号：31

期号：10

起止页码：1772

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20170703353670);Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金项目(61104178;61174040)

语种：中文

中文关键词：有限缓冲区;萤火虫算法;多目标优化;捕食搜索

外文关键词：limited buffers ; glowworm swarm optimization; multi-objectiveoptimization; predator search algorithm

摘要：针对缓冲区有限的多目标流水车间调度问题,提出一种基于Pareto最优的广义多目标萤火虫算法.通过引入交换子和交换序将基本萤火虫算法离散化,并将算法拓展为全局搜索过程和局部搜索过程.进化初期采用全局搜索将种群推向较优区域,进化中后期采用捕食搜索策略使算法主体在全局搜索和局部搜索间智能切换,从而保证全局与局部的平衡.动态变步长策略进一步增强了算法搜索能力.通过算例测试验证了所提出算法的有效性.
To deal with the multi-objective flow shop scheduling with limited buffer, a Pareto-based general multi-objective glowworm swarm optimization（GMOGSO） algorithm is proposed. The concepts of the swap operator and swap sequence are introduced to make the continuous GSO discrete. To balance the convergence speed and accuracy, the GSO algorithm is developed into two different processes including the global optimization one and local optimization one. The global optimization process is used to improve the quality of the initial population, then the predatory search strategy is used to coordinate local and global exploration. The method of variant step length enhances the exploratory capability. The proposed GMOGSO algorithm is compared to other algorithms, and the results show the effectiveness of the proposed algorithm.