Handling equality constraints in evolutionary optimization

详细信息	查看全文 | 推荐本文 |

• 作者：Abu S.S.M. Barkat Ullah ^{barkat.@adfa.edu.au} ; Ruhul Sarker ; ^{r.sarker.@adfa.edu.au} ; Chris Lokan ^{c.lokan.@adfa.edu.au}
• 关键词：Evolutionary algorithms ; Genetic algorithms ; Nonlinear programming ; Constrained optimization ; Equality constraints ; Search space
• 刊名：European Journal of Operational Research
• 出版年：2012
• 期刊代码：58_03772217
• 类别：et
• 出版时间：16 September, 2012
• 卷：221
• 期：3
• 页码：480-490
• 文件大小：1185 K

摘要

Over the last few decades several methods have been proposed for handling functional constraints while solving optimization problems using evolutionary algorithms (EAs). However, the presence of equality constraints makes the feasible space very small compared to the entire search space. As a consequence, the handling of equality constraints has long been a difficult issue for evolutionary optimization methods. This paper presents a Hybrid Evolutionary Algorithm (HEA) for solving optimization problems with both equality and inequality constraints. In HEA, we propose a new local search technique with special emphasis on equality constraints. The basic concept of the new technique is to reach a point on the equality constraint from the current position of an individual solution, and then explore on the constraint landscape. We believe this new concept will influence the future research direction for constrained optimization using population based algorithms. The proposed algorithm is tested on a set of standard benchmark problems. The results show that the proposed technique works very well on those benchmark problems.