PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation F. de Toro Negro a, * , J. Ortega b , E. Ros b , S. Mota b , B. Paechter c , J.M. Mart  ın a a Department of Electronic Engineering and Computer Science, University of Huelva, E.P.S. La Rabida, Crtra Huelva – La Rabida s/n, 21047 Huelva, Spain b Department of Computer Technology and Computer Architecture, ETSI inform atica, c/Daniel Saucedo Aranda, University of Granada, 18071 Granada, Spain c School of Computing, Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, Scotland, UK Received 10 November 2003; accepted 15 December 2003 Available online 20 May 2004 Abstract This paper deals with the study of the cooperation between parallel processing and evolu- tionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algo- rithm), 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 per- formance 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 outper- forms other previously proposed sequential procedures for multiobjective optimisation in the cases studied. Ó 2004 Elsevier B.V. All rights reserved. Keywords: Parallel evolutionary algorithms; Multiobjective optimisation; Cluster of computers * Corresponding author. E-mail address: ftoro@diesia.uhu.es (F. de Toro Negro). 0167-8191/$ - see front matter Ó 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.parco.2003.12.012 www.elsevier.com/locate/parco Parallel Computing 30 (2004) 721–739