全局引导和相互作用的郊狼优化算法及其应用.

Aiming at the problem, such as novel COA has slow convergence and insufficient global search ability when solving complex optimization problems, this paper proposed an algorithm called GCCOA. Firstly, during the growth of the coyotes in packs, it embedded a global-best guidance coyote to make the exploitation more powerful and to improve the convergence quality of COA. Then, it adopted a cultural tendency with coyo te interaction, which was impacted by the interaction of the coyotes in the pack, to improve the global search ability of COA. Finally, it applied GCCOA to complex function optimization and image enhancement. A lot of experimental results on 29 complex functions from CEC2017 set show that GCCOA obtains 22 times of the first place and has better optimization ability than COA, HFSPO, CSPSO and 13-GWO. In additional, the experimental results on medical image enhancement show that GCCOA can solve the optimization problem better than comparison algorithms such as COA and so on. So GCCOA is an optimization algorithm. [ABSTRACT FROM AUTHOR]