A Study of Distributed Evolutionary Algorithms for Multi-objective Optimisation

Most popular Evolutionary Algorithms for single multi-objective optimisation are motivated by the reduction of the computation time and the resolution larger problems. A promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. In the multi-objective optimisation problems, more exploration of the search space is required to obtain the whole or the best approximation of the Pareto front. Almost all proposed Parallel Multi-Objective Evolutionary Algorithms (PMOEAs) are based on the specialisation concept which means dividing the objective and/or the search space then assigning each part to a processor. One processor called the organiser or the coordinator is usually charged to direct the whole algorithm. In this paper, we present a new parallel scheme of multi-objective evolutionary algorithms which is based on a clustering technique. This new parallel algorithm is implemented and compared to three PMOEAs which are cone-separation [1], Divided Range Multi-Objective Genetic Algorithm (DRMOGA) [8] and a Parallel Strength Pareto Evolutionary Algorithm (PSPEA) based on the island model without migration.

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

[2]  Gary B. Lamont,et al.  Evolutionary algorithms for solving multi-objective problems, Second Edition , 2007, Genetic and evolutionary computation series.

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

[4]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[5]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithm test suites , 1999, SAC '99.

[6]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[7]  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).

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

[9]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

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

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

[12]  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).

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

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

[15]  Jeffrey Horn,et al.  The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems , 2001, EMO.

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

[17]  Mariem Gzara,et al.  Parallel Multi-Objective Evolutionary Algorithm with Multi-Front Equitable Distribution , 2006, 2006 Fifth International Conference on Grid and Cooperative Computing (GCC'06).

[18]  F. de Toro,et al.  PSFGA: a parallel genetic algorithm for multiobjective optimization , 2002, Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing.