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
杨开兵1
作者信息
- 1. 大连工业大学基础教学部,辽宁 大连 116034
- 折叠
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.
Abstract
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 ap-proximate Pareto optimal solutions.
关键词
流水车间调度/多目标优化/灰狼优化算法Key words
flow shop scheduling/multi-objective optimization/gray wolf optimization引用本文复制引用
基金项目
辽宁省教育厅科学研究项目(J2020108)
辽宁省教育厅科学研究项目(LJKZ0532)
出版年
2024
NETL
NSTL
万方数据