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A modified matrix adaptation evolution strategy with restarts for constrained real-world problems

  • In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the εMAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-εMAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original εMAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-εMAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.

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Metadaten
Author:Michael Hellwig, Hans-Georg Beyer
DOI:https://doi.org/10.1109/CEC48606.2020.9185566
ISBN:978-1-7281-6929-3
Parent Title (English):2020 IEEE Congress on Evolutionary Computation (CEC)
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of publication:2020
Release Date:2020/12/01
Tag:Constraints; Evolutionary algorithms; Optimization
Number og pages:8
Organisationseinheit:Technik / Department of Computer Science
Forschung / Forschungszentrum Business Informatics
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Open Access?:nein
Publicationlist:Beyer, Hans-Georg
Hellwig, Michael
Bibliographie 2020