Abstract
AbstractEvolutionary algorithms are used to solve optimization problems in a wide range of fields and are considered to be global optimization algorithms. However, evolutionary algorithms are limited in that they cannot be used to solve optimization problems with constraints. Additional methods to implement constraints must be used with these algorithms when solving constrained optimization problems. The purpose of the study is to improve the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to include constraints. An adaptive penalty function that is easy to implement, free of parameter tuning, and guaranteed to find a solution for every problem at every run was used to impose constraints on the SCE-UA. The modified SCE-UA was validated by application to two constrained optimization problems. The algorithm was also applied to an automatic calibration of the storm water management model (SWMM), which is a hydrological model. An automatic calibration by unconstrained optimization (the ori...
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 callDisclaimer: 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.
