摘要
针对多仓库问题,首先使用聚类分析将多仓库问题转换为多个单仓库问题,并将人工鱼群算法的觅食行为引入和声搜索算法,同时采用自适应策略动态调整和声搜索算法的音调微调概率和音调微调带宽。仿真结果表明:相对于基本和声搜索算法及其他三种常见启发式算法,所设计的算法具有更好的收敛速度和收敛精度。
Aiming at the problem of logistics transportation scheduling in multi-warehouse process production,the paper designed a harmony search algorithm based on the foraging behavior of artificial fish swarm algorithm.For the multi-warehouse problem,the clustering analysis is firstly used to transform the multi-warehouse problem into multiple single-warehouse problems,and the foraging behavior of the artificial fish swarm algorithm is introduced into the harmony search algorithm,and the adaptive strategy is used to dynamically adjust the acoustic search algorithm.Tone fine-tuning probability and pitch fine-tuning bandwidth.The simulation results show that,compared with the basic harmony search algorithm and three other common heuristic algorithms,the designed algorithm has better convergence speed and convergence precision.
作者
李帅
蔡延光
蔡颢
张丽
LI Shuai;CAI Yanguang;CAI Hao;ZHANG Li(School of Automation, Guangdong University of Technology, Guangzhou 510006, China;Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark)
出处
《东莞理工学院学报》
2020年第3期34-40,共7页
Journal of Dongguan University of Technology
基金
国家自然科学基金(61074147)
广东省自然科学基金(S2011010005059)
广东省教育部产学研结合项目(2012B091000171,2011B090400460)
广东省科技计划项目(2012B050600028,2014B010118004,2016A050502060)
广州市花都区科技计划项目(HD14ZD001)
广州市科技计划项目(201604016055)。
关键词
和声搜索算法
人工鱼群算法
聚类分析
自适应策略
流程生产
物流运输调度
harmony search algorithm
artificial fish swarm algorithm
cluster analysis
adaptive strategy
process production
logistics and transportation scheduling