Probabilistic evolutionary bound constraint handling for particle swarm optimization

Probabilistic evolutionary bound constraint handling for particle swarm optimization Oper Res Int J https://doi.org/10.1007/s12351-018-0401-6 ORIGINAL PAPER Probabilistic evolutionary bound constraint handling for particle swarm optimization 1 2 Amir H. Gandomi  · Ali R. Kashani Received: 28 March 2017 / Revised: 15 February 2018 / Accepted: 9 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Keeping the search space between the valid domains is one of the most important necessities for most of the optimization problems. Among the optimi- zation algorithms, particle swarm optimization (PSO) is highly likely to violate boundary limitations easily because of its oscillating behavior. Therefore, PSO is led to be sensitive to bound constraint handling (BCH) method. This matter has not been taken to account very much until now. This study attempt to apply and explore the efficiency of one of the most recent BCH schemes called evolutionary boundary constraint handling (EBCH) on PSO. In addition, probabilistic evolutionary bound- ary constraint handling (PEBCH) is also introduced in this study as an update on EBCH approach. As a complementary step of previous efforts, in the current docu - ment, PSO with both EBCH and PEBCH are utilized to solve several benchmark functions and the results are compared to other approaches in the literature. The results reveal that, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Operational Research Springer Journals

Probabilistic evolutionary bound constraint handling for particle swarm optimization

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Operations Research, Management Science; Computational Intelligence
ISSN
1109-2858
eISSN
1866-1505
D.O.I.
10.1007/s12351-018-0401-6
Publisher site
See Article on Publisher Site

Abstract

Oper Res Int J https://doi.org/10.1007/s12351-018-0401-6 ORIGINAL PAPER Probabilistic evolutionary bound constraint handling for particle swarm optimization 1 2 Amir H. Gandomi  · Ali R. Kashani Received: 28 March 2017 / Revised: 15 February 2018 / Accepted: 9 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Keeping the search space between the valid domains is one of the most important necessities for most of the optimization problems. Among the optimi- zation algorithms, particle swarm optimization (PSO) is highly likely to violate boundary limitations easily because of its oscillating behavior. Therefore, PSO is led to be sensitive to bound constraint handling (BCH) method. This matter has not been taken to account very much until now. This study attempt to apply and explore the efficiency of one of the most recent BCH schemes called evolutionary boundary constraint handling (EBCH) on PSO. In addition, probabilistic evolutionary bound- ary constraint handling (PEBCH) is also introduced in this study as an update on EBCH approach. As a complementary step of previous efforts, in the current docu - ment, PSO with both EBCH and PEBCH are utilized to solve several benchmark functions and the results are compared to other approaches in the literature. The results reveal that,

Journal

Operational ResearchSpringer Journals

Published: May 29, 2018

References

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