Abstract
Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self-adaptive HGA (SAHGA) and compares its performance to a nonadaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Water Resources Planning and Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
