To address the problems of weak global search capability and easy to fall into local optimal in the basic salp swarm algorithm(SSA),an improved SSA algorithm based on adaptive global optimal guidance mechanism and adaptive control factor is proposed.Firstly,the adaptive global optimal guidance mechanism is introduced in the leader position update phase,which effectively improves the global search ability of the algorithm.Secondly,the adaptive control factor is introduced in the follower position update phase,which greatly improves the local search ability of the algorithm.To verify the optimization performance of the proposed algorithm,six unimodal,seven multimodal benchmark functions,and 29 CEC 2017 test functions are employed for experiments.The experimental results show that the overall performance of the developed algorithm is better than the basic SSA,several SSA variants,and other frontiers comparison algorithms under the same number of iterations.
关键词
樽海鞘群算法/全局最优引导/自适应控制因子/全局优化
Key words
Salp swarm algorithm/Global best guidance/Adaptive control factor/Global optimization
维普
万方数据