Title: Mutation strategies toward Pareto front for multi-objective differential evolution algorithm

Authors: Warisa Wisittipanich; Voratas Kachitvichyanukul

Addresses: Industrial and Manufacturing Engineering, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand ' Industrial and Manufacturing Engineering, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand

Abstract: This paper presents a multi-objective differential evolution algorithm, called MODE, to search for a set of non-dominated solutions on the Pareto front. During the iterative search process, the non-dominated solutions found are stored as the 'Elite group' of solutions. The study focuses on utilising the solutions in the Elite group to guide the movement of the search. Several potential mutation strategies in MODE framework are proposed as the movement guidance in order to obtain the high-quality front. Each mutation strategy possesses distinct search behaviour which directs a vector in the DE population in different ways with the purpose of reaching the Pareto optimal front. The performance of the proposed algorithm is evaluated on a set of well-known benchmark problems and compared with results from other existing approaches. The experimental results demonstrate that the proposed MODE algorithm is a highly competitive approach for solving multi-objective optimisation problems.

Keywords: mutation strategies; multi-objective problems; Pareto front; evolutionary algorithms; differential evolution.

DOI: 10.1504/IJOR.2014.059507

International Journal of Operational Research, 2014 Vol.19 No.3, pp.315 - 337

Received: 25 Apr 2012
Accepted: 02 Sep 2012
Published online: 17 Jun 2014 *

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