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Generation of Pareto optimal solutions using generalized DEA and PSO

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  • Yeboon Yun
  • Hirotaka Nakayama
  • Min Yoon

Abstract

Meta-heuristic methods such as particle swarm optimization and genetic algorithms have been applied in solving multi-objective optimization problems, and have been observed to be useful for generating a good approximation of Pareto optimal solutions. This paper suggests a multi-objective particle swarm optimization (MOPSO) utilizing generalized data envelopment analysis (GDEA) in order to decide adaptively parameters of MOPSO as well as to improve the convergence and the diversity in the search of solutions. In addition, the effectiveness of the proposed method using GDEA will be investigated by comparison with conventional methods through several numerical examples. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Yeboon Yun & Hirotaka Nakayama & Min Yoon, 2016. "Generation of Pareto optimal solutions using generalized DEA and PSO," Journal of Global Optimization, Springer, vol. 64(1), pages 49-61, January.
    • Handle: RePEc:spr:jglopt:v:64:y:2016:i:1:p:49-61
    DOI: 10.1007/s10898-015-0314-3
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    Cited by:

    1. Alexander P. Afanasiev & Vladimir E. Krivonozhko & Andrey V. Lychev & Oleg V. Sukhoroslov, 2020. "Multidimensional frontier visualization based on optimization methods using parallel computations," Journal of Global Optimization, Springer, vol. 76(3), pages 563-574, March.
    2. Yeşilyurt, M. Ensar & Şahin, Emre & Elbi, M. Doğan & Kızılkaya, Aydın & Koyuncuoğlu, M. Ulaş & Akbaş-Yeşilyurt, Filiz, 2021. "A novel method for computing single output for DEA with application in hospital efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).

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