Efficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOA

被引：36
|
作者：
Aittokoski, T. [1 ]
Miettinen, K. [1 ]
机构：
[1] Univ Jyvaskyla, Dept Math Informat Technol, FI-40014 Agora, Finland
来源：
OPTIMIZATION METHODS & SOFTWARE | 2010年 / 25卷 / 06期
关键词：
efficient Pareto-optimal set approximation; multicriteria optimization; population-based approaches; Pareto-optimality; non-dominance; EMO; DECISION-MAKING; CONVERGENCE; ADAPTATION; ALGORITHM;
D O I：
10.1080/10556780903548265
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Solving real-life engineering problems requires often multiobjective, global, and efficient (in terms of objective function evaluations) treatment. In this study, we consider problems of this type by discussing some drawbacks of the current methods and then introduce a new population-based multiobjective optimization algorithm UPS-EMOA which produces a dense (not limited to the population size) approximation of the Pareto-optimal set in a computationally effective manner.
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页码：841 / 858
页数：18
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