Multimedia Information systems and Advanced Computing Laboratory (MIRACL)
route Tunis Km 3; B.P. 1030, Sfax, 3018
Laboratoire de Gestion Industrielle et d’Aide à la Décision (GIAD)
route de l’aérodrome Km 4.5 BP 1088, Sfax, 3018
Abstract: Most parallel evolutionary algorithms for single and multi-objective optimisation are motivated by the reduction of the computation time and the resolution of larger problems. Another promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. In multi-objective optimisation problems, more exploration of the search space is required to obtain the whole or the best approximation of the Pareto Optimal Front. In this paper, we present a new clustering-based parallel multi-objective evolutionary algorithm that balances between the two main concepts in metaheuristics, which are exploration and exploitation of the search space. The proposed algorithm is implemented and tested on several standard multi-objective test functions using a network of multiple computers.
Keywords: Parallel computing, multi-objective optimisation, evolutionary algorithms.
CITE THIS PAPER AS:
Mariem GZARA, Abdelbasset ESSABRI, Balanced Explore-Exploit clustering based Distributed Evolutionary Algorithm for Multi-objective Optimisation, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (2), pp. 97-106, 2011.