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A mapping technique for better solution exploration: NSGA-II adaptation

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  • Julien Autuori

    (Université de Technologie de Troyes)

  • Faicel Hnaien

    (Université de Technologie de Troyes)

  • Farouk Yalaoui

    (Université de Technologie de Troyes)

Abstract

A mapping method (MaM) for a better solution space exploration adapted to NSGA-II method is presented. The Mapping technique divides the solution space into several zones using a Hamming distance to a reference solution. We present a bijective mapping function from the search space to the binary representation space of solutions. For each zone, a mapping metric is used to evaluate the solution space exploration. According to this evaluation, a local search is performed. The mapping is adapted to the well known non-dominated sorting genetic algorithm-II (NSGA-II) method applied to solve the flexible job shop problem (FJSP) case. We present the comparison between the hybridization using the local search for the non-dominated solutions and the hybridization using the mapping metrics. The multi-objective metrics show the efficiency of mapping adaptation in terms of convergence and diversity.

Suggested Citation

  • Julien Autuori & Faicel Hnaien & Farouk Yalaoui, 2016. "A mapping technique for better solution exploration: NSGA-II adaptation," Journal of Heuristics, Springer, vol. 22(1), pages 89-123, February.
    • Handle: RePEc:spr:joheur:v:22:y:2016:i:1:d:10.1007_s10732-015-9303-4
    DOI: 10.1007/s10732-015-9303-4
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    1. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    2. Eugeniusz Nowicki & Czeslaw Smutnicki, 1996. "A Fast Taboo Search Algorithm for the Job Shop Problem," Management Science, INFORMS, vol. 42(6), pages 797-813, June.
    3. Huang, Rong-Hwa & Yang, Chang-Lin & Cheng, Wei-Che, 2013. "Flexible job shop scheduling with due window—a two-pheromone ant colony approach," International Journal of Production Economics, Elsevier, vol. 141(2), pages 685-697.
    4. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
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