On Mixed-Discrete Nonlinear Optimization Problems: A Comparative Study

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Title:	On Mixed-Discrete Nonlinear Optimization Problems: A Comparative Study
Authors:	Lin, S.S.;Zhang, C.;Wang, H.P.
Date:	1995
Issue Date:	2009-08-19 09:53:54 (UTC+8)
Abstract:	Modified genetic algorithms are developed and presented in this paper. Principles of genetics and natural selection are adapted into the search procedure for mixed-discrete nonlinear optimization problems. Such classes of global optimization algorithms are based on a randomized selection of design space that yields an improvement in the objective function. An implementation of the approach to a series of test problems in engineering design optimization with diversity of variable representations and demonstrated nonconvexities are discussed, and the results were compared with other algorithms. Results show that genetic algorithms are able to consistently provide efficient, fine quality solutions, that are robust to genetic parameters and provide a significant capability for mixed-discrete constrained nonlinear optimization problems.
Relation:	Engineering Optimization, 23, 287-300
Appears in Collections:	[Department of Business Administration] 【企業管理系所】期刊論文

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