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An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method
Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
- 1 Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
- 2 Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
- 3 Energy Informatics Laboratory, Faculty of Engineering & Applied Science. University of Regina, Regina, SK, Canada
Journal of Environmental Protection·Volume 08 (2017)·Pages 231–249·Published 10 March 2017·DOI10.4236/jep.2017.83018
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Abstract
In this paper, an innovative Genetic Algorithms (GA) - based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA- based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLAB TM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involve d uncertainty.
KeywordsGenetic AlgorithmsInexact Non-Linear Programming (INLP)Economy of ScaleNumeric OptimizationSolid Waste Management
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