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An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

Author

Listed:
  • Aristotelis E. Thanos

    (Industrial Engineering, The University of Miami, Coral Gables, FL, USA)

  • Nurcin Celik

    (Industrial Engineering, The University of Miami, Coral Gables, FL, USA)

  • Juan P. Sáenz

    (Industrial Engineering, The University of Miami, Coral Gables, FL, USA)

Abstract

In this paper, we present a novel sequential sampling methodology for solving multi-objective optimization problems. Random sequential sampling is performed using the information from within the non-dominated solution set generated by the algorithm, while resampling is performed using the extreme points of the non-dominated solution set. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms that exist in the literature through a series of problem instances. The proposed algorithm has been demonstrated to perform at least as good as the alternatives found in the literature in problems where the Pareto front presents convexity, nonconvexity, or discontinuity; while producing very promising results in problem instances where there is multi-modality or nonuniform distribution of the solutions along the Pareto front.

Suggested Citation

  • Aristotelis E. Thanos & Nurcin Celik & Juan P. Sáenz, 2016. "An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(01), pages 1-21, February.
    • Handle: RePEc:wsi:apjorx:v:33:y:2016:i:01:n:s0217595916500068
    DOI: 10.1142/S0217595916500068
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    References listed on IDEAS

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    1. Jaszkiewicz, Andrzej, 2002. "Genetic local search for multi-objective combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 137(1), pages 50-71, February.
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