Salp swarm algorithm (SSA) is a new swarm intelligence optimization algorithm, which has the advantages of simple structure, almost no parameter setting. However, SSA also has the shortcomings of slow convergence speed in the early stage and low optimization accuracy in the later stage when searching for the optimal solution. To address the problems, this study proposes a hybrid chaos-cloud salp swarm algorithm (CC-SSA). First, to accelerate the convergence speed, a positive normal cloud generator is used to perform local search on the superior salp individuals. Second, to enhance the diversity of CC-SSA and avoid it from falling into local optimum, the chaotic map is used to perform global search on the inferior salp individuals by adding global perturbation. Third, to control the execution ratio of global and local search, the mixed control parameter (ML) and population allocation coefficient () are introduced to organically combine three algorithms, SSA, chaotic map, and cloud model. Finally, to evaluate the performance of the proposed algorithm, it is compared with other 9 conventional algorithms on 12 classic functions. The experimental results show that CC-SSA has an average accuracy rate of 92.92 %, which ranks first. In addition, the average iteration of CC-SSA scores the third. Therefore, compared to conventional optimization algorithms, CC-SSA has better performance in terms of execution time and optimize accuracy.

中文翻译：

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混合混沌-coud樽海鞘群算法研究


Salp群体算法（SSA）是一种新型群体智能优化算法，具有结构简单、几乎不需要参数设置的优点。但SSA在搜索最优解时也存在前期收敛速度慢、后期优化精度低的缺点。为了解决这些问题，本研究提出了一种混合混沌云樽海鞘群算法（CC-SSA）。首先，为了加快收敛速度​​，使用正法线云生成器对优秀樽海鞘个体进行局部搜索。其次，为了增强CC-SSA的多样性，避免陷入局部最优，利用混沌映射加入全局扰动，对劣等樽海鞘个体进行全局搜索。第三，为了控制全局和局部搜索的执行比例，引入混合控制参数（ML）和种群分配系数（）将SSA、混沌映射和云模型三种算法有机地结合起来。最后，为了评估该算法的性能，在12个经典函数上与其他9种传统算法进行了比较。实验结果表明CC-SSA平均准确率为92.92%，排名第一。此外，CC-SSA的平均迭代得分排名第三。因此，与传统的优化算法相比，CC-SSA在执行时间和优化精度方面具有更好的性能。