A hybrid particle swarm optimization algorithm for the capacitated location routing problem. Issue 1 (12th March 2018)

• Record Type: Journal Article
• Authors: Kechmane, Laila; Nsiri, Benayad; Baalal, Azeddine
• Is Part Of: International journal of intelligent computing and cybernetics. Volume 11:Issue 1(2018)
• Page Start: 106
• Page End: 120
• Publication Date: 2018-03-12
• Subjects: Particle swarm optimization -- Variable neighbourhood search -- Capacitated location routing problem; Artificial intelligence -- Periodicals; Cybernetics -- Periodicals; 006.3
• DOI: 10.1108/IJICC-03-2017-0023
• Languages: English
• ISSNs: 1756-378X

Abstract

Abstract : Purpose: The purpose of this paper is to solve the capacitated location routing problem (CLRP), which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions, using a hybrid particle swarm optimization (PSO) algorithm. Design/methodology/approach: PSO, which is a population-based metaheuristic, is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP. Findings: The algorithm is tested on a set of instances available in the literature and gave good quality solutions, results are compared to those obtained by other metaheuristic, evolutionary and PSO algorithms. Originality/value: Local search is a time consuming phase in hybrid PSO algorithms, a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase, moves are applied directly to particles, a clear decoding method is adopted to evaluate a particle (solution) and there is no need to re-encode solutions in the form of particles after applying local search.