A diversity preserving selection in multiobjective evolutionary algorithms

被引：0
作者：
Chang Wook Ahn
R. S. Ramakrishna
机构：
[1] Sungkyunkwan University,School of Information and Communication Engineering
[2] Gwangju Institute of Science and Technology,Department of Information and Communications
来源：
Applied Intelligence | 2010年 / 32卷
关键词：
Diversity preservation; Elitism; Multiobjective optimization; Multiobjective evolution algorithms; Scaled objectives; Selection;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, an efficient diversity preserving selection (DPS) technique is presented for multiobjective evolutionary algorithms (MEAs). The main goal is to preserve diversity of nondominated solutions in problems with scaled objectives. This is achieved with the help of a mechanism that preserves certain inferior individuals over successive generations with a view to provide long term advantages. The mechanism selects a group (of individuals) that is statistically furthest from the worst group, instead of just concentrating on the best individuals, as in truncation selection. In a way, DPS judiciously combines the diversity preserving mechanism with conventional truncation selection. Experiments demonstrate that DPS significantly improves diversity of nondominated solutions in badly-scaling problems, while at the same time it exhibits acceptable proximity performance. Whilst DPS has certain advantages when it comes to scaling problems, it empirically shows no disadvantages for the problems with non-scaled objectives.
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页码：231 / 248
页数：17
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