A Hybrid Butterfly Optimization Algorithm for Numerical Optimization Problems.

Authors :: Zhou H
Cheng HY
Wei ZL
Zhao X
Tang AD
Xie L
Source :: Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 Dec 24; Vol. 2021, pp. 7981670. Date of Electronic Publication: 2021 Dec 24 (Print Publication: 2021).
Publication Year :: 2021
Abstract: The butterfly optimization algorithm (BOA) is a swarm-based metaheuristic algorithm inspired by the foraging behaviour and information sharing of butterflies. BOA has been applied to various fields of optimization problems due to its performance. However, BOA also suffers from drawbacks such as diminished population diversity and the tendency to get trapped in local optimum. In this paper, a hybrid butterfly optimization algorithm based on a Gaussian distribution estimation strategy, called GDEBOA, is proposed. A Gaussian distribution estimation strategy is used to sample dominant population information and thus modify the evolutionary direction of butterfly populations, improving the exploitation and exploration capabilities of the algorithm. To evaluate the superiority of the proposed algorithm, GDEBOA was compared with six state-of-the-art algorithms in CEC2017. In addition, GDEBOA was employed to solve the UAV path planning problem. The simulation results show that GDEBOA is highly competitive.<br />Competing Interests: The authors declare no conflicts of interest.<br /> (Copyright © 2021 Huan Zhou et al.)