A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization

Date
2014-11-15Author
Melo, Vinicius Veloso de [UNIFESP]
Iacca, Giovanni
Type
ArtigoISSN
0957-4174Is part of
Expert Systems With ApplicationsDOI
10.1016/j.eswa.2014.06.032Metadata
Show full item recordAbstract
In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. the presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. in this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. the proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling. (C) 2014 Elsevier B.V. All rights reserved.
Citation
Expert Systems With Applications. Oxford: Pergamon-Elsevier B.V., v. 41, n. 16, p. 7077-7094, 2014.Keywords
Constrained optimizationCovariance Matrix Adaptation Evolution Strategy
Adaptive penalty function
Sponsorship
Province of DrentheMunicipality of Assen
European Fund for Regional Development
Ministry of Economic Affairs
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Peaks in the Delta
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