Treffer: Solving the vehicle routing problem by using cellular genetic algorithms

Title:
Solving the vehicle routing problem by using cellular genetic algorithms
Authors:
ALBA, Enrique, DORRONSORO, Bernabé
Source:
EvoCOP 2004 : evolutionary computation in combinatorial optimization (Coimbra, 5-7 April 2004)Lecture notes in computer science. :11-20
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Flots dans les réseaux. Problèmes combinatoires, Flows in networks. Combinatorial problems, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Diversité, Diversity, Diversidad, Evaluation fonction, Function evaluation, Localisation, Localization, Localización, Méthode heuristique, Heuristic method, Método heurístico, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Optimum, Optimo, Problème tournée véhicule, Vehicle routing problem, Problema ruta vehículo, Temps exécution, Execution time, Tiempo ejecución, Algorithme cellulaire, Cellular algorithm, Algoritmo celular, Recherche locale, Local search, Busca local
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, University of Málaga, Spain
Central Computer Services, University of Málaga, Spain
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=15758678
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.15758678
Database:
PASCAL Archive

Weitere Informationen

Cellular Genetic Algorithms (cGAs) are a subclass of Genetic Algorithms (GAs) in which the population diversity and exploration are enhanced thanks to the existence of small overlapped neighborhoods. Such a kind of structured algorithms is specially well suited for complex problems. In this paper we propose the utilization of some cGAs with and without including local search techniques for solving the vehicle routing problem (VRP). A study on the behavior of these algorithms has been performed in terms of the quality of the solutions found, execution time, and number of function evaluations (effort). We have selected the benchmark of Christofides, Mingozzi and Toth for testing the proposed cGAs, and compare them with some other heuristics in the literature. Our conclusions are that cGAs with an added local search operator are able of always locating the optimum of the problem at low times and reasonable effort for the tested instances.