Abstract
In general, this paper is focused on creating a fuzzy combination of two optimization algorithms. In this case, the algorithms work with populations and allow us to migrate between them every certain number of iterations. On the other hand, fuzzy logic is responsible for the dynamic adjustment of parameters within each of the algorithms since the variables are different in each algorithm. In previous works, a combination between genetic algorithm and particle swarm optimization was developed, which motivated us to create this combination expecting to obtain better results when compared to the previous works. The moth-flame optimization and lightning search algorithm were combined to obtain a powerful hybrid metaheuristic combining the advantages of both individual algorithms.
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.
