A differential evolution algorithm for constrained multi-objective optimization: Initial assessment

被引：0
|
作者：
Kukkonen, S [1 ]
Lampinen, J [1 ]
机构：
[1] Lappeenranta Univ Technol, Dept Informat Technol, FIN-53851 Lappeenranta, Finland
来源：
PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, VOLS 1AND 2 | 2004年
关键词：
multi-objective optimization; Pareto-optimization; constraints; evolution algorithms; differential evolution;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper an Evolutionary Algorithm, the Differential Evolution algorithm, and its extension for constrained multi-objective optimization are described. The described extension is tested with a set of five benchmark multiobjective test problems and one constrained multi-objective test problem. Control parameter values for these test problems are surveyed and recommendations for initial control parameter values are concluded. The results are compared to known global Pareto-optimal fronts and to results obtained with the Strength Pareto Evolutionary Algorithm in the case of benchmark problems. Results show that the extension is well comparable to the performance of the Strength Pareto Evolutionary Algorithm.
引用
收藏
页码：96 / 102
页数：7
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