DocumentCode
722433
Title
A particle swarm optimization with adaptive multi-swarm strategy for capacitated vehicle routing problem
Author
Kui-Ting Chen ; Yijun Dai ; Ke Fan ; Baba, Takaaki
Author_Institution
Res. Center, Waseda Univ., Fukuoka, Japan
fYear
2015
fDate
2-4 March 2015
Firstpage
79
Lastpage
83
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 an optimal 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.
Keywords
combinatorial mathematics; costing; goods distribution; minimisation; particle swarm optimisation; search problems; vehicle routing; AMSPSO; CVRPPD; adaptive algorithm; adaptive multiswarm strategy; capacitated vehicle routing problem with pickups and delivery; combinatorial optimization problem; goods delivery/pickup optimization; large-scale optimization problem; particle swarm optimization; punishment mechanism; routing path optimization; search strategy; transportation cost minimization; vehicle number optimization; Benchmark testing; Optimization; Particle swarm optimization; Routing; Vehicle routing; Vehicles; adaptive algorithm; multi-swarm; particle swarm optimization; vehicle routing problem;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Networks and Intelligent Systems (INISCom), 2015 1st International Conference on
Conference_Location
Tokyo
Type
conf
DOI
10.4108/icst.iniscom.2015.258972
Filename
7157825
Link To Document