多策略融合的改进樽海鞘群算法.

In order to solve the problems of low convergence accuracy and difficulty in jumping out of local optimum of traditional SSA, this paper proposed an improved salp swarm algorithm (ISSA) with multi-strategy fusion. Firstly, this paper proposed a new follower position update method integrating the convergence strategy of the midperpendicular algorithm to solve the shortcomings of the traditional SSA follower position update method. In order to improve the ability of SSA to jump out of the local optimum, this paper proposed a self perturbation strategy based on the convergence strategy of the midperpendicular algorithm. Secondly, by analyzing the shortcomings of the traditional SSA leader position update strategy, this paper proposed a new leader position update strategy, and for the fixed population order of SSA, this paper proposed a method to reconstruct the arrangement order of the salp population with the index of fitness to improve the performance of the algorithm. Finally, simulation experiments prove the effectiveness of the ISSA, and the experiments results show that ISSA solves the problems of low convergence accuracy and difficulty in jumping out of local optimum, and improves the convergence speed and stability of SSA. The superiority of ISSA is proved by comparison experiments with other improved SSA. [ABSTRACT FROM AUTHOR]