Genetic Algorithm for Constrained Optimization Models and Its Application in Groundwater Resources Management

Abstract

Genetic algorithms (GAs) have been shown to be an efficient tool for the solution of unconstrained optimization problems. In their standard form, GA formulations are “blind” to the constraints of an optimization model when the model involves these constraints. Thus, in GA applications alternative procedures are used to satisfy the constraints of the optimization model. In this study, the method that is utilized in the Complex Algorithm to solve constrained optimization problems is abstracted to develop a repairing procedure for GAs. The proposed procedure, which handles infeasible solutions that may be generated in a standard GA process, is embedded into the conventional GA to yield an improved GA process (IGA) for the solution of optimization problems with equality and inequality constrains. Two numerical examples are included to demonstrate the effectiveness and efficiency of the proposed method for the solution of constrained optimization applications. Finally the IGA is successfully used to develop an optimal groundwater management plan for the Savannah, Ga. region.