A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10247257" target="_blank" >RIV/61989100:27240/20:10247257 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/20:10247257
Result on the web
<a href="https://dl.acm.org/doi/pdf/10.1145/3377929.3398185" target="_blank" >https://dl.acm.org/doi/pdf/10.1145/3377929.3398185</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3377929.3398185" target="_blank" >10.1145/3377929.3398185</a>

Result language
angličtina
Original language name
A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problems
Original language description
Most of the real-world black-box optimization problems are associated with multiple non-linear as well as non-convex constraints, making them difficult to solve. In this work, we introduce a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with linear timing complexity to adopt the constraints of Constrained Optimization Problems (COPs). CMA-ES is already well-known as a powerful algorithm for solving continuous, non-convex, and black-box optimization problems by fitting a second-order model to the underlying objective function (similar in spirit, to the Hessian approximation used by Quasi-Newton methods in mathematical programming). The proposed algorithm utilizes an e-constraint-based ranking and a repair method to handle the violation of the constraints. The experimental results on a group of real-world optimization problems show that the performance of the proposed algorithm is better than several other state-of-the-art algorithms in terms of constraint handling and robustness. (C) 2020 Owner/Author.
Czech name
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Czech description
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Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference
ISBN
978-1-4503-7128-5
ISSN
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e-ISSN
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Number of pages
2
Pages from-to
11-12
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Cancún
Event date
Jul 8, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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