Generation of Pareto optimal solutions using generalized DEA and PSO

Yeboon Yun (), Hirotaka Nakayama () and Min Yoon ()

Journal of Global Optimization, 2016, vol. 64, issue 1, 49-61

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

Keywords: Multi-objective optimization; Pareto optimal solutions; Generalized data envelopment analysis; Particle swarm optimization (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10898-015-0314-3

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