Enhancements to Genetic Algorithm for Optimal Ground-Water Management

Abstract

Genetic algorithm (GA) is considered to be a robust technique for solving ground-water optimization problems. Often these problems are difficult to solve using traditional gradient-based techniques as these are nonlinear, nonconvex, and discontinuous. In this manuscript, recent research related to application of GA in solving these problems is critically reviewed, and three areas of potential enhancement to GA are identified and explored. These enhancement methods to GA are fitness reduction method (FRM), search bound sampling method (SBSM), and optimal resource allocation guideline (ORAG). In order to assess these methods, a nonlinear ground-water problem with fixed and variable costs is selected (from literature) where the corresponding optimal solution using a gradient-based nonlinear programming (NLP) technique is available. The problem is resolved using GA coupled with the enhancement methods, and the GA solutions are compared with the NLP solution. In addition, the sensitivity of these methods to various GA parameters are studied. The results of the analysis using the enhancement methods show the following: (1) FRM enhances efficiency of GA in handling constraints; (2) SBSM enhances accuracy of GA in solving problems with fixed costs; (3) ORAG enhances reliability of GA by providing some convergence guarantee for a given computational resource; and (4) when applied with FRM and SBSM, accuracy of GA is marginally increased from near-optimal to global-optimal by tuning any one of the several parameters. There appears to be little need to embark on a full-scale analysis for achieving the increase.