Abstract
Dragonfly algorithm is an intelligent group optimization algorithm. In this paper, dragonfly algorithm is well used in the field of feature selection. However, dragonfly algorithm has the problem of falling into local optimal solution, which reduces the performance of feature selection and classification. Therefore, this paper proposes a binary Dragonfly optimization algorithm based on spark, which integrates the global optimization ability of dragonfly algorithm with the parallel computing ability of spark, greatly improving the performance of the algorithm. Experimental results show that binary Dragonfly algorithm has better performance than traditional particle swarm optimization algorithm and genetic algorithm. The binary Dragonfly algorithm based on spark solves the problem of falling into the local optimal solution, improves the running speed of the algorithm, can effectively deal with massive data, and greatly enhances the performance of the algorithm.
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.
