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A New Hybrid Evolutionary Multiobjective Algorithm Guided by Descent Directions

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Journal of Mathematical Modelling and Algorithms in Operations Research

Abstract

Hybridization of local search based algorithms with evolutionary algorithms is still an under-explored research area in multiobjective optimization. In this paper, we propose a new multiobjective algorithm based on a local search method. The main idea is to generate new non-dominated solutions by adding a linear combination of descent directions of the objective functions to a parent solution. Additionally, a strategy based on subpopulations is implemented to avoid the direct computation of descent directions for the entire population. The evaluation of the proposed algorithm is performed on a set of benchmark test problems allowing a comparison with the most representative state-of-the-art multiobjective algorithms. The results show that the proposed approach is highly competitive in terms of the quality of non-dominated solutions and robustness.

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Authors and Affiliations

  1. Algoritmi R&D Center, University of Minho, Minho, Portugal

    Roman Denysiuk

  2. Department of Production and Systems Engineering, University of Minho, Minho, Portugal

    Lino Costa & Isabel Espírito Santo

Authors
  1. Roman Denysiuk
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  2. Lino Costa
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  3. Isabel Espírito Santo
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Correspondence to Roman Denysiuk.

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Denysiuk, R., Costa, L. & Espírito Santo, I. A New Hybrid Evolutionary Multiobjective Algorithm Guided by Descent Directions. J Math Model Algor 12, 233–251 (2013). https://doi.org/10.1007/s10852-012-9208-2

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  • DOI: https://doi.org/10.1007/s10852-012-9208-2

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