Generation of Pareto optimal solutions using generalized DEA and PSO

被引：0
|
作者：
Yeboon Yun
Hirotaka Nakayama
Min Yoon
机构：
[1] Kansai University,
[2] Konan University,undefined
[3] Pukyong National University,undefined
来源：
Journal of Global Optimization | 2016年 / 64卷
关键词：
Multi-objective optimization; Pareto optimal solutions ; Generalized data envelopment analysis; Particle swarm optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：49 / 61
页数：12
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