Research Article
A Particle Swarm Optimization with Adaptive Multi-Swarm Strategy for Capacitated Vehicle Routing Problem
@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
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.
Copyright © 2015 Kui-Ting Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.