Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems

Generalized polynomial programming (GPP) is a nonlinear programming (NLP) method based on a nonconvex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimizat...

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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 11; H. 4; S. 358 - 364
Hauptverfasser: Wu, Jui-Yu, Chung, Yun-Kung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 01.04.2007
ISSN:1343-0130, 1883-8014
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Abstract Generalized polynomial programming (GPP) is a nonlinear programming (NLP) method based on a nonconvex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimization, and therefore do not easily solve GPP problems. Some deterministic global optimization approaches have been developed to overcome this drawback of NLP methods. Although these approaches yield a global solution to a GPP problem, they can be mathematically tedious. Therefore, this study presents a real-coded genetic algorithm (RGA), which is a stochastic global optimization method, to find a global solution to a GPP problem. The proposed RGA is used to solve a set of GPP problems. The best solution obtained by the RGA is compared with the known global solution to each test problem. Numerical results show that the proposed RGA converges to a global solution to a GPP problem.
AbstractList Generalized polynomial programming (GPP) is a nonlinear programming (NLP) method based on a nonconvex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimization, and therefore do not easily solve GPP problems. Some deterministic global optimization approaches have been developed to overcome this drawback of NLP methods. Although these approaches yield a global solution to a GPP problem, they can be mathematically tedious. Therefore, this study presents a real-coded genetic algorithm (RGA), which is a stochastic global optimization method, to find a global solution to a GPP problem. The proposed RGA is used to solve a set of GPP problems. The best solution obtained by the RGA is compared with the known global solution to each test problem. Numerical results show that the proposed RGA converges to a global solution to a GPP problem.
Author Chung, Yun-Kung
Wu, Jui-Yu
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CitedBy_id crossref_primary_10_1016_j_neucom_2017_05_054
crossref_primary_10_1155_2012_841410
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10.1137/0115117
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10.1109/CEC.2002.1004391
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CorporateAuthor Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan
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Title Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems
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