Open AccessAn adaptive ES with a ranking based constraint handling strategy
Author(s) -
Ali Osman Kuşakçı,
Mehmet Can
Publication year - 2014
Publication title -
yugoslav journal of operations research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.221
H-Index - 21
eISSN - 1820-743X
pISSN - 0354-0243
DOI - 10.2298/yjor140121035k
Subject(s) - mathematical optimization , constraint (computer aided design) , benchmark (surveying) , cma es , evolution strategy , computer science , operator (biology) , evolutionary algorithm , ranking (information retrieval) , variable (mathematics) , mathematics , artificial intelligence , geography , geodesy , gene , geometry , transcription factor , chemistry , repressor , biochemistry , mathematical analysis
To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs) do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES) along with a simplified Covariance Matrix Adaptation (CMA) based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods
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