求解多目标流水车间调度问题的改进灰狼算法  

Improved Grey Wolf Optimization for Solving Multi-objective Flow Shop Scheduling Problem

作　　者：杨开兵 YANG Kai-bing(Dalian Polytechnic University,Dalian 116034,China)

机构地区：[1]大连工业大学基础教学部,辽宁大连116034

出　　处：《电脑与电信》2024年第7期13-16,共4页Computer & Telecommunication

基　　金：辽宁省教育厅科学研究项目,项目编号:J2020108,LJKZ0532。

摘　　要：为高效求解多目标流水车间调度问题,提出一种改进的多目标灰狼优化算法。该算法采用多阶段搜索与灰狼自主搜寻的混合策略,提高算法的全局搜索能力和局部搜索能力。同时为使算法适用于多目标离散调度问题,提出一种基于交叉操作的离散个体更新方法。实验结果表明,所提算法不仅提高了优化搜索的效率,而且能够找到更多的Pareto最优解。To efficiently solve the multi-objective permutation flow shop scheduling problems,this paper proposes an improved multi-objective gray wolf optimization algorithm.The algorithm introduces a hybridization strategy which combining multi-stage search strategy with walk guard strategy to enhance the search capabilities of the algorithm.At the same time,it presents a discrete individual updating method based on crossover operation to make the algorithm suitable for the multi-objective discrete scheduling problem.The experimental results show that the proposed algorithm can improve search efficiency of optimization and find more approximate Pareto optimal solutions.

关 键 词：流水车间调度 多目标优化 灰狼优化算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]