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An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization

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  • Ishibuchi, Hisao
  • Narukawa, Kaname
  • Tsukamoto, Noritaka
  • Nojima, Yusuke

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  • Ishibuchi, Hisao & Narukawa, Kaname & Tsukamoto, Noritaka & Nojima, Yusuke, 2008. "An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 188(1), pages 57-75, July.
    • Handle: RePEc:eee:ejores:v:188:y:2008:i:1:p:57-75
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    References listed on IDEAS

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    1. Jaszkiewicz, Andrzej, 2004. "On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study," European Journal of Operational Research, Elsevier, vol. 158(2), pages 418-433, October.
    2. 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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    1. Goh, C.K. & Tan, K.C. & Liu, D.S. & Chiam, S.C., 2010. "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design," European Journal of Operational Research, Elsevier, vol. 202(1), pages 42-54, April.
    2. Wang, Yujia & Yang, Yupu, 2010. "Particle swarm with equilibrium strategy of selection for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 200(1), pages 187-197, January.
    3. Sahinkoc, H. Mert & Bilge, Ümit, 2022. "A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem," European Journal of Operational Research, Elsevier, vol. 300(2), pages 405-417.

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