An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy.

ABSTRACT: Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught

SUBMITTER: Wang X 

PROVIDER: S-EPMC10204464 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature