TITLE:
An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method
AUTHORS:
Weihua Jin, Zhiying Hu, Christine Chan
KEYWORDS:
Genetic Algorithms, Inexact Non-Linear Programming (INLP), Economy of Scale, Numeric Optimization, Solid Waste Management
JOURNAL NAME:
Journal of Environmental Protection,
Vol.8 No.3,
March
14,
2017
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
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.