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Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches

Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches

Pandian Vasant
Copyright: © 2012 |Pages: 25
ISBN13: 9781613501382|ISBN10: 1613501382|EISBN13: 9781613501399
DOI: 10.4018/978-1-61350-138-2.ch012
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MLA

Vasant, Pandian. "Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches." Innovation in Power, Control, and Optimization: Emerging Energy Technologies, edited by Pandian Vasant, et al., IGI Global, 2012, pp. 344-368. https://doi.org/10.4018/978-1-61350-138-2.ch012

APA

Vasant, P. (2012). Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches. In P. Vasant, N. Barsoum, & J. Webb (Eds.), Innovation in Power, Control, and Optimization: Emerging Energy Technologies (pp. 344-368). IGI Global. https://doi.org/10.4018/978-1-61350-138-2.ch012

Chicago

Vasant, Pandian. "Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches." In Innovation in Power, Control, and Optimization: Emerging Energy Technologies, edited by Pandian Vasant, Nadar Barsoum, and Jeffrey Webb, 344-368. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-138-2.ch012

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Abstract

In this chapter a solution is proposed to a certain nonlinear programming difficulties related to the presence of uncertain technological coefficients represented by vague numbers. Only vague numbers with modified s-curve membership functions are considered. The proposed methodology consists of novel genetic algorithms and a hybrid genetic algorithm pattern search (Vasant, 2008) for nonlinear programming for solving problems that arise in industrial production planning in uncertain environments. Real life application examples in production planning and their numerical solutions are analyzed in detail. The new method suggested has produced good results in finding globally near-optimal solutions for the objective function under consideration.

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