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An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

Author

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  • Ali, Musrrat.
  • Siarry, Patrick
  • Pant, Millie.

Abstract

In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEAs). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm.

Suggested Citation

  • Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:2:p:404-416
    DOI: 10.1016/j.ejor.2011.09.025
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    1. Deb, Kalyanmoy & Tiwari, Santosh, 2008. "Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1062-1087, March.
    2. Kaelo, P. & Ali, M.M., 2006. "A numerical study of some modified differential evolution algorithms," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1176-1184, March.
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