多策略融合的改进樽海鞘群算法

为解决传统樽海鞘群算法(SSA)收敛精度低、难以跳出局部最优等问题，提出了一种多策略融合的改进樽海鞘群算法(ISSA)。首先，提出了一种新的融合中垂线算法收敛策略的追随者位置更新方法，以解决传统SSA追随者位置更新方法的不足；为提升SSA跳出局部最优的能力，提出一种基于中垂线算法收敛策略的自扰动策略。其次，通过分析传统SSA领导者位置更新策略存在的不足，提出了一种新的领导者位置更新策略，并针对SSA的固定种群顺序，提出了以适应度为指标重构樽海鞘群体排列顺序的方法以提升算法性能。最后以仿真实验对ISSA的性能进行了验证，结果表明ISSA解决了SSA收敛精度低和难以跳出局部最优的问题，提升了SSA的收敛速度和稳定性。通过与其他改进SSA的对比实验，证明了ISSA的优越性。

Improved salp swarm algorithm with multi-strategy fusion

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 algorithm is proved by comparison experiments with other improved SSA.