A differential mutation operator for the archive population of multi-objective evolutionary algorithms

Authors:
Lucas S. Batista;Frederico G. Guimarães;Jaime A. Ramírez
Affiliations:
Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil;Department of Computer Science, Universidade Federal de Ouro Preto, Ouro Preto, Brazil;Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The Differential Evolution (DE) algorithm is a simple and efficient evolutionary algorithm that has been applied to solve many optimization problems mainly in continuous search domains. In the last few years, many implementations of multi-objective versions of DE have been proposed in the literature, combining the traditional differential mutation operator as the variation mechanism and some form of Pareto-ranking based fitness. In this paper, we propose the utilization of the differential mutation operator as an additional operator to be used within any multi-objective evolutionary algorithm that employs an archive (offline) population. The operator is applied for improving the high-quality solutions stored in the archive, working both as a local search operator and a diversity operator depending on the points selected to build the differential mutation. In order to illustrate the use of the operator, it is coupled with the NSGA-II and the multi-objective DE (MODE), showing promising results.