PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation

de Toro Negro, F, Ortega, J, Ros, E, Mota, S, Paechter, Ben and Martin, J M (2004) PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing, 30 (5-6). pp. 551-816. ISSN 0167-8191

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This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algorithm), an elitist evolutionary algorithm for multiobjective problems with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes. Thus, PSFGA is a parallel genetic algorithm with a structured population in the form of a set of islands. The performance analysis of PSFGA has been carried out in a cluster system and experimental results show that our parallel algorithm provides adequate results in both, the quality of the solutions found and the time to obtain them. It has been shown that its sequential version also outperforms other previously proposed sequential procedures for multiobjective optimisation in the cases studied

Item Type: Article
Print ISSN: 0167-8191
Uncontrolled Keywords: cluster of computers; multiobjective optimisation; parallel evolutionary algorithms
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 3371
Depositing User: Computing Research
Date Deposited: 06 May 2010 16:18
Last Modified: 07 Jun 2013 12:25

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