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Multiobjective variable mesh optimization

  • S.I.: CLAIO 2014
  • Published:
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Abstract

In this article we introduce a new multiobjective optimizer based on a recently proposed metaheuristic algorithm named Variable Mesh Optimization (VMO). Our proposal (multiobjective VMO, MOVMO) combines typical concepts from the multiobjective optimization arena such as Pareto dominance, density estimation and external archive storage. MOVMO also features a crossover operator between local and global optima as well as dynamic population replacement. We evaluated MOVMO using a suite of four well-known benchmark function families, and against seven state-of-the-art optimizers: NSGA-II, SPEA2, MOCell, AbYSS, SMPSO, MOEA/D and MOEA/D.DRA. The statistically validated results across the additive epsilon, spread and hypervolume quality indicators confirm that MOVMO is indeed a competitive and effective method for multiobjective optimization of numerical spaces.

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Notes

  1. The parameter values for each benchmarking algorithm have been drawn from their original articles.

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Acknowledgments

This work was funded by the eureka SD project (agreement number 2013-2591), that is supported by the Erasmus Mundus programme of the European Union.

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Authors and Affiliations

  1. Informatics Department, University of Las Tunas, Las Tunas, Cuba

    Yamisleydi Salgueiro & Jorge L. Toro

  2. Computer Science Department, Central University of Las Villas, Santa Clara, Cuba

    Rafael Bello

  3. Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Rafael Falcon

Authors
  1. Yamisleydi Salgueiro
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  2. Jorge L. Toro
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  3. Rafael Bello
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  4. Rafael Falcon
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Correspondence to Yamisleydi Salgueiro.

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Salgueiro, Y., Toro, J.L., Bello, R. et al. Multiobjective variable mesh optimization. Ann Oper Res 258, 869–893 (2017). https://doi.org/10.1007/s10479-016-2221-5

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  • DOI: https://doi.org/10.1007/s10479-016-2221-5

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