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

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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.

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Authors and Affiliations

  1. ICD-LOSI, Université de Technologie de Troyes, 12 rue Marie Curie, CS42060, 10004, Troyes Cedex, France

    Julien Autuori, Faicel Hnaien & Farouk Yalaoui

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  1. Julien Autuori
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  2. Faicel Hnaien
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  3. Farouk Yalaoui
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Correspondence to Julien Autuori.

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Autuori, J., Hnaien, F. & Yalaoui, F. A mapping technique for better solution exploration: NSGA-II adaptation. J Heuristics 22, 89–123 (2016). https://doi.org/10.1007/s10732-015-9303-4

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  • DOI: https://doi.org/10.1007/s10732-015-9303-4

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