Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

中文翻译：

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具有交叉方案和Lévy飞行的Salp群算法进行全局优化

Salp群算法（SSA）是近年来提出的一种元启发式算法，在解决某些优化任务方面显示出一定的优势。然而，随着解决问题的难度增加（例如多模态，高维），SSA算法的收敛精度和稳定性下降。为了克服这些缺点，提出了具有交叉方案和LévyFlight（SSACL）的salp群算法。交叉方案和Lévy飞行策略分别用于改善后腰领先者和追随者的运动方式。已经对各种测试功能进行了实验，包括单峰，多峰和复合函数。实验结果表明，本文提出的SSACL算法在精度，稳定性和效率方面均优于其他高级算法。此外，