Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247739" target="_blank" >RIV/61989100:27240/21:10247739 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/21:10247739

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2210650221000973" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650221000973</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2021.100936" target="_blank" >10.1016/j.swevo.2021.100936</a>

Result language

angličtina

Original language name

Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Original language description

The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms. (C) 2021 The Author(s)

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

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Volume of the periodical

65

Issue of the periodical within the volume

65

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

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UT code for WoS article

000680430000017

EID of the result in the Scopus database

2-s2.0-85108854822