Authors:
Kamel Belkhelladi
1
;
Pierre Chauvet
1
and
Arnaud Schaal
2
Affiliations:
1
LISA, Université d’Angers; CREAM, Université Catholique de l’Ouest, France
;
2
CREAM, Université Catholique de l’Ouest, France
Keyword(s):
Capacitated Arc Routing Problem, Information exchange strategy, Distributed computing, Agents.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
Distributed computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and have been implemented on parallel computers to speed up the computation. In this paper, we introduce a multi-agent model conceived as a conceptual and practical framework for distributed genetic algorithms used both to reduce execution time and get closer to optimal solutions. Instead of using expensive parallel computing facilities, our distributed model is implemented on easily available networked personal computers (PCs). In order to show that the parallel co-evolution of different sub-populations may lead to an efficient search strategy, we design a new information exchange strategy based on different dynamic migration window methods and a selective migration model. To evaluate the proposed approach, different kinds of experiments have been conducted on an extended set of Capacitated Arc Routing Problem(CARP). Obtained results are useful for optimization pra
ctitioners and show the efficiency of our approach.
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