DIFFERENTIAL EVOLUTION FOR SOLVING MULTIOBJECTIVE OPTIMIZATION PROBLEMS

Ruhul Sarker () and Hussein A. Abbass ()
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Ruhul Sarker: School of Information Technology and Electrical Engineering, University of New South Wales, ADFA Campus, Northcott Drive, Canberra, ACT, 2600, Australia
Hussein A. Abbass: School of Information Technology and Electrical Engineering, University of New South Wales, ADFA Campus, Northcott Drive, Canberra, ACT, 2600, Australia

Asia-Pacific Journal of Operational Research (APJOR), 2004, vol. 21, issue 02, 225-240

Abstract: The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the "strength Pareto evolutionary algorithm", one of the state-of-the-art evolutionary algorithm for solving VOPs.

Keywords: Multi-objective optimization; vector optimization; evolutionary strategies; differential evolution; Pareto frontier; population-based approach (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0217595904000217

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