Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.

中文翻译：

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一种基于扰动权重的改进Salp Swarm算法求解全局优化问题

元启发式算法通常应用于全局函数优化问题。为了克服基本salp算法的实时性差和精度低的问题，提出了一种基于扰动权重机制的新型混合算法。所提出的摄动权重小群算法具有搜索范围广，探索与开发之间的平衡强的优点，并且在处理众多大规模问题时保持了较低的计算复杂度。在基本salp群算法中引入了新的系数因子，并在搜索阶段引入了针对领导者位置和跟随者的新更新策略。新的领导者位置更新策略具有特定的范围限制和强大的搜索性能，从而加快了迭代过程。新的关注者更新策略在减少计算量的同时，保持了可行解决方案的多样性。本文介绍了该算法在低维和变维函数中的应用。本文还提出了迭代曲线，箱形图和搜索路径图形，以验证所提出算法的准确性。实验结果表明，在不同环境下，扰动权重小群算法提供了比基本小群算法更好的搜索速度和搜索平衡。搜索路径图形以验证所提出算法的准确性。实验结果表明，在不同环境下，扰动权重小群算法提供了比基本小群算法更好的搜索速度和搜索平衡。搜索路径图形以验证所提出算法的准确性。实验结果表明，在不同环境下，扰动权重小群算法提供了比基本小群算法更好的搜索速度和搜索平衡。