Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems

This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in per...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 197; no. 33; pp. 3080 - 3091
Main Authors: Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., Alizadeh, Y.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2008
Elsevier
Subjects:
Applied sciences
Engineering optimization
Exact sciences and technology
Flows in networks. Combinatorial problems
Harmony search algorithm
Heuristic
Hybridization
Mechanical engineering. Machine design
Operational research and scientific management
Operational research. Management science
Sequential quadratic programming
Hybridization
Sequential quadratic programming
Engineering optimization
Heuristic
Harmony search algorithm
Local search
Empirical method
Combinatorial optimization
Quadratic programming
Modeling
Search algorithm
Convergence
Engineering design
Sequential method
Heuristic method
Hybrid model
Robustness
Deterministic approach
Metamodel
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in performing local search. In this study sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions. Moreover, an empirical study is performed in order to determine the impact of various parameters of the HSA on convergence behavior. Various benchmark engineering optimization problems are used to illustrate the effectiveness and robustness of the proposed algorithm. Numerical results reveal that the proposed hybrid algorithm, in most cases is more effective than the HSA and other meta-heuristic or deterministic methods.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2008.02.006