An Improved Salp Swarm Algorithm Based on Cosine Decline and Chaos Crossover Strategies

To solve the problem of low convergence precision and imbalance between exploitation and exploration abilities in the optimization process of salp swarm algorithm (SSA), an improved version based on the strategies of cosine decline and chaos crossover (CDCSSA) is proposed. The position updating of the leader in SSA is optimized by the cosine decline strategy, which enhances the local exploitation and global exploration abilities of the algorithm. The crossover strategy based on Logistic chaotic map helps followers to improve the precision of convergence and effectively jump out of the local optimal solution. The experimental results show that CDCSSA has a good performance in balancing exploitation and exploration abilities. Compared with other algorithms, CDCSSA achieves faster convergence speed and higher precision on 8 single-peak and multi-peak benchmark functions.