Abstract
Differential evolution is a simple but powerful parallel global search optimization algorithm, which has been successfully used to solve single-objective optimization problems. In this paper, a novel discrete differential evolution strategy (D2E) is proposed to enhance the ability of solving the numerical optimization problems. D2E adopts the difference information from the binary evolutionary individuals and self-adaptive parameter adjustment to effectively explore the global solution space. The results of numerical simulations demonstrate that the proposed strategy is feasible and efficient, has the better metrics performances.
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.
