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

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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.

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Notes

  1. These algorithms are implemented by jMetal [7], a Java-based framework of meta-heuristic algorithms for finding Pareto optimal solutions, while our algorithm is implemented in MatLab. The jMetal source files can be downloaded from the web-site  http://sourceforge.net/projects/jmetal/.

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Authors and Affiliations

  1. Faculty of Environmental and Urban Engineering, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka, 564-8680, Japan

    Yeboon Yun

  2. Department of Intelligence and Informatics, Konan University, 8-9-1 Okamoto, Higashinada, Kobe, 658-8501, Japan

    Hirotaka Nakayama

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  1. Yeboon Yun
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Yun, Y., Nakayama, H. Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms. J Glob Optim 57, 367–384 (2013). https://doi.org/10.1007/s10898-013-0038-1

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