[1]杨文强,张素君,郭昊.求解仓储作业优化问题的多物种协同进化算法[J].郑州大学学报(工学版),2020,41(06):33-39.[doi:10.13705/j.issn.1671-6833.2019.03.030]
　YANG Wenqiang,ZHANG Sujun,GUO Hao.Operation Optimization of Warehousing by Multispecies Co-evolution Algorithm[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):33-39.[doi:10.13705/j.issn.1671-6833.2019.03.030]
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求解仓储作业优化问题的多物种协同进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年06期
页码:
33-39
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Operation Optimization of Warehousing by Multispecies Co-evolution Algorithm
作者:
杨文强; 张素君; 郭昊
河南科技学院机电学院,河南新乡453003, 河南科技学院机电学院,河南新乡453003, 河南科技学院机电学院,河南新乡453003

Author(s):
YANG Wenqiang;  ZHANG Sujun;  GUO Hao
School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
关键词:
Keywords:
warehouse operation optimization;  genetic algorithm;  particle swarm optimization algorithm;  artificial fish swarm algorithm;  multispecies co-evolution
DOI:
10.13705/j.issn.1671-6833.2019.03.030
文献标志码:
A
摘要:
针对一类带过道仓储作业优化问题，为提高仓储作业效率，受生物进化论的启发，提出基于遗传、粒子群以及人工鱼群共同参与的多物种协同进化算法（Multispecies Co-evolution Algorithm, MSCA），即通过基于学习机制的多物种竞争共生捕食策略，使每个物种适应环境的能力都能得到增强；同时引入变异机制，使全部物种的种群多样性得到协同改善，从而在提高单个物种进化能力的同时，也提高了算法的全局寻优能力及求解效率，最后通过工业现场实例验证了算法的有效性.
Abstract:
To improve the efficiency of warehousing operation, inspired by biological evolutionism, a new method of multispecies co-evolution algorithm (MSCA) based on genetic algorithm, particle swarm optimization algorithm, and artificial fish swarm algorithm was proposed to solve warehousing operation optimization problem with cross aisles in this paper. It improved the ability of each species to adapt to one′s environment based on multispecies competition-predatory strategies by learning mechanism; and could improve the population diversity of all the species cooperatively using mutation mechanism. It enchanced evolutional capacity of single species, and further improved the global search ability and solving efficiency of MSCA. Finally, industrial example showed the effectiveness of the proposed algorithm.

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更新日期/Last Update: 2021-02-10