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Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Author

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  • Juan Li

    (School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China
    School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Dan-dan Xiao

    (School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China)

  • Hong Lei

    (School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Ting Zhang

    (School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China)

  • Tian Tian

    (School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China)

Abstract

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q -Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

Suggested Citation

  • Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:149-:d:311424
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    References listed on IDEAS

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    Cited by:

    1. Juan Li & Yuan-Hua Yang & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Moth Search: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
    2. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    3. Jun Wu & Xin Liu & Yuanyuan Li & Liping Yang & Wenyan Yuan & Yile Ba, 2022. "A Two-Stage Model with an Improved Clustering Algorithm for a Distribution Center Location Problem under Uncertainty," Mathematics, MDPI, vol. 10(14), pages 1-17, July.

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