An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

• 作者: Christine Chan Weihua Jin Zhiying Hu
• 来源期刊: 环境保护(英文)
• 学科: 数学
• 关键词: Genetic ALGORITHMS INEXACT NON-LINEAR PROGRAMMING (INLP) ECONOMY of Scale Numeric Optimization Solid Waste Management
• 年，卷（期）: 2017,（3）
• 页码范围: 231-249
• 页数: 19页
• 分类号: O1

摘要

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 MATLABTM 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 involved uncertainty.