research-article

Cooperative coevolutionary differential evolution with improved augmented Lagrangian to solve constrained optimisation problems

Authors:

Behrooz Ghasemishabankareh,

Melih OzlenAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 369, Issue C

Pages 441 - 456

https://doi.org/10.1016/j.ins.2016.06.047

Published: 10 November 2016 Publication History

Abstract

Propose the improved augmented Lagrangian method (iALF) to deal with constraints in a more efficient way.Propose cooperative coevolutionary method using improved augmented Lagrangian (CCiALF) approach for solving constrained optimisation problems.Solve the CEC'2006 constrained optimisation test suite with proposed CCiALF algorithm with higher solution quality compared to the state-of-the-art algorithms.Solve practical engineering problems by proposed CCiALF algorithm and improve the best known objective function value that exists in the literature. In constrained optimisation, the augmented Lagrangian method is considered as one of the most effective and efficient methods. This paper studies the behaviour of augmented Lagrangian function (ALF) in the solution space and then proposes an improved augmented Lagrangian method. We have shown that our proposed method can overcome some of the drawbacks of the conventional augmented Lagrangian method. With the improved augmented Lagrangian approach, this paper then proposes a cooperative coevolutionary differential evolution algorithm for solving constrained optimisation problems. The proposed algorithm is evaluated on a set of 24 well-known benchmark functions and five practical engineering problems. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms with respect to solution quality as well as efficiency.

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Cited By

Reddy RGupta UKale IShastri AKulkarni A(2024)Modified Leader-Advocate-Believer Algorithm with Clustering-Based Search Space Reduction Method for Solving Engineering Design ProblemsSN Computer Science10.1007/s42979-024-02716-55:4Online publication date: 29-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02716-5
de Melo VNascimento AIacca G(2024)A co-evolutionary algorithm with adaptive penalty function for constrained optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09896-528:19(11343-11376)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00500-024-09896-5
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Cooperative coevolutionary differential evolution with improved augmented Lagrangian to solve constrained optimisation problems
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 369, Issue C

November 2016

791 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 10 November 2016

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Cited By

Reddy RGupta UKale IShastri AKulkarni A(2024)Modified Leader-Advocate-Believer Algorithm with Clustering-Based Search Space Reduction Method for Solving Engineering Design ProblemsSN Computer Science10.1007/s42979-024-02716-55:4Online publication date: 29-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02716-5
de Melo VNascimento AIacca G(2024)A co-evolutionary algorithm with adaptive penalty function for constrained optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09896-528:19(11343-11376)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00500-024-09896-5
Nokhandan BKhalili-Damghani KHafezalkotob ADidehkhani H(2021)A Nash bargaining solution for a multi period competitive portfolio optimization problemExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115509184:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115509
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Xu BTao LChen XCheng W(2019)Adaptive differential evolution with multi-population-based mutation operators for constrained optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-3001-023:10(3423-3447)Online publication date: 1-May-2019
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Yu XLu YWang XLuo XCai M(2019)An effective improved differential evolution algorithm to solve constrained optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2936-523:7(2409-2427)Online publication date: 1-Apr-2019
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Salim MAhmed A(2018)A quasi-Newton augmented Lagrangian algorithm for constrained optimization problemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1789935:2(2373-2382)Online publication date: 1-Jan-2018
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Panda APani S(2018)An orthogonal parallel symbiotic organism search algorithm embodied with augmented Lagrange multiplier for solving constrained optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2693-522:8(2429-2447)Online publication date: 1-Apr-2018
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