inis 15(5): e3

Research Article

A Particle Swarm Optimization with Adaptive Multi-Swarm Strategy for Capacitated Vehicle Routing Problem

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  • @ARTICLE{10.4108/eai.17-9-2015.150285,
        author={Kui-Ting  Chen and Ke  Fan and Yijun  Dai and Takaaki  Baba},
        title={A Particle Swarm Optimization with Adaptive Multi-Swarm Strategy for Capacitated Vehicle Routing Problem},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={2},
        number={5},
        publisher={EAI},
        journal_a={INIS},
        year={2015},
        month={9},
        keywords={multi-swarm, particle swarm optimization, vehicle routing problem, adaptive algorithm},
        doi={10.4108/eai.17-9-2015.150285}
    }
    
  • Kui-Ting Chen
    Ke Fan
    Yijun Dai
    Takaaki Baba
    Year: 2015
    A Particle Swarm Optimization with Adaptive Multi-Swarm Strategy for Capacitated Vehicle Routing Problem
    INIS
    EAI
    DOI: 10.4108/eai.17-9-2015.150285
Kui-Ting Chen1,*, Ke Fan1, Yijun Dai1, Takaaki Baba1
  • 1: Research Center and Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Kitakyushu, Fukuoka, Japan
*Contact email: nore@aoni.waseda.jp

Abstract

Capacitated vehicle routing problem with pickups and deliveries (CVRPPD) is one of the most challenging combinatorial optimization problems which include goods delivery/pickup optimization, vehicle number optimization, routing path optimization and transportation cost minimization. The conventional particle swarm optimization (PSO) is difficult to find a n o p timal solution of the CVRPPD due to its simple search strategy. A PSO with adaptive multi-swarm strategy (AMSPSO) is proposed to solve the CVRPPD in this paper. The proposed AMSPSO employs multiple PSO algorithms and an adaptive algorithm with punishment mechanism to search the optimal solution, which can deal with large-scale optimization problems. The simulation results prove that the proposed AMSPSO can solve the CVRPPD with the least number of vehicles and less transportation cost, simultaneously.