Cuckoo-Beetle Swarm Search for Nonlinear Optimization: A New Meta-Heuristic Algorithm

Abstract

Conventional meta-heuristic algorithms have problems being trapped in local optima because of premature convergence and insufficient global search ability in solving nonlinear optimization. This brief proposes a new meta-heuristic algorithm, called cuckoo-beetle swarm search (CBSS) to handle this issue in nonlinear optimization. The proposed algorithm imitates the breeding habits of cuckoos and the food foraging characteristics of beetles. Levy flight is combined with particle search velocity to improve the global optimization of CBSS. Nonlinear benchmark functions are tested to verify the accuracy, search speed, and stability of the CBSS algorithm. The numerical verification proves that the proposed algorithm has better robustness and global search capability than its counterparts.