A hybrid algorithm based on ACO and PSO for capacitated vehicle routing problems
type: Item
instance of: scholarly article
title: A hybrid algorithm based on ACO and PSO for capacitated vehicle routing problems
zbMATH Open document ID: 1264.90018
DOI: 10.1155/2012/726564
author: Yucheng Kao
author: Ming-Hsien Chen
author: Yi-Ting Huang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: The vehicle routing problem (VRP) is a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligence (SI) approaches, ant colony optimization (ACO) and particle swarm optimization (PSO), for solving capacitated vehicle routing problems (CVRPs). In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.
Mathematics Subject Classification ID: 90B06
Mathematics Subject Classification ID: 90C35
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6173520
Wikidata QID: Q58911640
describes a project that uses: VRP
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/726564
OpenAlex ID: W2133962014
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description: scientific article; zbMATH DE number 6173520