A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper a...

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Main Authors: Fatemeh, D. B., Loo, C. K., Kanagaraj, G., Ponnambalam, S. G.
Format: Article
Language:English
Published: Universiti Malaysia Pahang 2018
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/1/A%20hybrid%20SP-QPSO%20algorithm%20with%20parameter.pdf
id ump-22212
recordtype eprints
spelling ump-222122018-11-12T01:44:05Z http://umpir.ump.edu.my/id/eprint/22212/ A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems Fatemeh, D. B. Loo, C. K. Kanagaraj, G. Ponnambalam, S. G. TS Manufactures Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time. Universiti Malaysia Pahang 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22212/1/A%20hybrid%20SP-QPSO%20algorithm%20with%20parameter.pdf Fatemeh, D. B. and Loo, C. K. and Kanagaraj, G. and Ponnambalam, S. G. (2018) A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems. Journal of Modern Manufacturing Systems and Technology, 1. pp. 15-26. ISSN 2636-9575 http://journal.ump.edu.my/jmmst/article/view/195 DOI: https://doi.org/10.15282/jmmst.v1i1.195
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Fatemeh, D. B.
Loo, C. K.
Kanagaraj, G.
Ponnambalam, S. G.
A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
description Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time.
format Article
author Fatemeh, D. B.
Loo, C. K.
Kanagaraj, G.
Ponnambalam, S. G.
author_facet Fatemeh, D. B.
Loo, C. K.
Kanagaraj, G.
Ponnambalam, S. G.
author_sort Fatemeh, D. B.
title A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
title_short A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
title_full A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
title_fullStr A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
title_full_unstemmed A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems
title_sort hybrid sp-qpso algorithm with parameter free adaptive penalty method for constrained global optimization problems
publisher Universiti Malaysia Pahang
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/
http://umpir.ump.edu.my/id/eprint/22212/1/A%20hybrid%20SP-QPSO%20algorithm%20with%20parameter.pdf
first_indexed 2023-09-18T22:32:56Z
last_indexed 2023-09-18T22:32:56Z
_version_ 1777416406858465280

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