Parallel Multi-Objective Evolutionary Algorithm with Multi-Front Equitable Distribution

In multi-objective context, the evolutionary approach offers specific mechanisms such as Pareto selection, elitism and diversification. These techniques are proved to be efficient to characterize the Pareto front. However, their high computing time constitutes a major handicap for their expansion. The parallelization of multi-objective evolutionary algorithms (MOEAs) may be an efficient way to overcome this problem. This parallelization aims not only to achieve time saving by distributing the computational effort but also to get benefit from the algorithmic aspect by the cooperation between different populations and evolutionary schemes. In this paper we propose a new parallel multi-objective evolutionary algorithm with multi-front equitable distribution which is based on an elitist technique. Every population evolves differently on a processor and cooperates with the others to preserve genetic diversity and to obtain a set of diversified non dominated solutions

[1]  Tomoyuki Hiroyasu,et al.  Distributed genetic algorithms with a new sharing approach in multiobjective optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[3]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[4]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[5]  Tomoyuki Hiroyasu,et al.  A parallel genetic algorithm with distributed environment scheme , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[6]  Kai Xu,et al.  A scalable parallel genetic algorithm for x-ray spectroscopic analysis , 2005, GECCO '05.

[7]  Kalyanmoy Deb,et al.  Parallelizing multi-objective evolutionary algorithms: cone separation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Geoffrey C. Fox,et al.  Proceedings of the 4th international conference on Grid and Cooperative Computing , 2005 .

[9]  Tomoyuki Hiroyasu,et al.  MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme , 2005, Evolutionary Multiobjective Optimization.

[10]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .