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Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

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  • Yeboon Yun
  • Hirotaka Nakayama

Abstract

Meta-heuristic methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been extended to multi-objective optimization problems, and have been observed to be useful for finding good approximate Pareto optimal solutions. In order to improve the convergence and the diversity in the search of solutions using meta-heuristic methods, this paper suggests a new method to make offspring by utilizing the expected improvement (EI) and generalized data envelopment analysis (GDEA). In addition, the effectiveness of the proposed method will be investigated through several numerical examples in comparison with the conventional multi-objective GA and PSO methods. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Yeboon Yun & Hirotaka Nakayama, 2013. "Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms," Journal of Global Optimization, Springer, vol. 57(2), pages 367-384, October.
    • Handle: RePEc:spr:jglopt:v:57:y:2013:i:2:p:367-384
    DOI: 10.1007/s10898-013-0038-1
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    References listed on IDEAS

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    1. Yun, Y. B. & Nakayama, H. & Arakawa, M., 2004. "Multiple criteria decision making with generalized DEA and an aspiration level method," European Journal of Operational Research, Elsevier, vol. 158(3), pages 697-706, November.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Yun, Y. B. & Nakayama, H. & Tanino, T., 2004. "A generalized model for data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 157(1), pages 87-105, August.
    4. Yun, Y. B. & Nakayama, H. & Tanino, T. & Arakawa, M., 2001. "Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 129(3), pages 586-595, March.
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    Cited by:

    1. Kourosh Ranjbar & Hamid Khaloozadeh & Aghileh Heydari, 2020. "A novel mixed Spider’s web initial solution and data envelopment analysis for solving multi-objective optimization problems," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 193-208, March.

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