 Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling ProblemsYadong Yu, Haiping Ma, Mei Yu, Sengang Ye and Xiaolei Chen Complexity, 2018, vol. 2018, 1-14 Abstract: Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4730957 DOI: 10.1155/2018/4730957 Access Statistics for this article More articles in Complexity from Hindawi |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.