Abstract
Modifications to the standard genetic algorithm through a finetuning strategy, a hillclimbing strategy and the use of independent subpopulations coupled with shuffling are described. The improvements obtained are demonstrated using two optimization problems; a continuous variable rainfall-runoff model calibration and a previously-studied mixed discrete-continuous optimization for cost minimization in pressure vessel manufacture. The use of independent Subpopulations and shuffling is found to considerably improve optimizations of the two problems whilst the finetuning and hillclimbing notably improve optimization in the model calibration but not the pressure vessel cost minimization. In the rainfall-runoff modelling the parameter sets obtained by the improved genetic algorithm are more consistent and seem more informative than those obtained with the standard genetic algorithm. With the pressure vessel design problem, lower costs are obtained than in previous studies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.