The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.

中文翻译：

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自适应模糊鸡群优化算法

鸡群优化（CSO）算法是一种新的群体智能优化（SIO）算法，已广泛应用于许多工程领域。但是，CSO算法存在两个明显的问题，即收敛速度慢和难以实现全局最优解。为了解决公民社会组织的这两个问题，本文提出了一种自适应模糊鸡群优化算法。所提出的FCSO使用模糊系统来自适应地调整CSO算法的鸡的数量和随机因素，并实现算法的开发和探索能力的最佳平衡。我们将余弦函数集成到FCSO中，以计算公鸡的位置更新并提高收敛速度。我们将FCSO与八种常用的进行了比较，就低维和高维空间的性能而言，都是最先进的SIO算法。我们还使用非参数统计弗里德曼检验来验证FCSO算法。30个黑盒优化基准测试（BBOB）函数的实验结果表明，我们的FCSO在收敛速度和优化精度方面均优于其他SIO算法。为了进一步测试FCSO算法的适用性，我们将其应用于四个对优化过程有所限制的典型工程问题。结果表明，与标准的CSO算法相比，FCSO的优化精度更高。30个黑盒优化基准测试（BBOB）函数的实验结果表明，我们的FCSO在收敛速度和优化精度方面均优于其他SIO算法。为了进一步测试FCSO算法的适用性，我们将其应用于四个对优化过程有所限制的典型工程问题。结果表明，与标准的CSO算法相比，FCSO的优化精度更高。30个黑盒优化基准测试（BBOB）函数的实验结果表明，我们的FCSO在收敛速度和优化精度方面均优于其他SIO算法。为了进一步测试FCSO算法的适用性，我们将其应用于四个对优化过程有所限制的典型工程问题。结果表明，与标准的CSO算法相比，FCSO的优化精度更高。