*2.1. KH*

At present, we have two kinds of optimizations for the KH algorithm [49]; one is to improve the KH algorithm itself, and the other is to improve the KH by other excellent operators or algorithms. Wang et al. [54] changed the parameters of the KH algorithm and improved the speed of global convergence through chaos theory. Then, in order to improve the performance, they [66] used harmony search to replace the physical diffusion, which greatly improved the performance and efficiency. These algorithms belong to the first category, which optimized the KH algorithm itself. The following are the second category, which optimized the algorithm with better strategies. Abualigah et al. [50] put forward a new algorithm, which combined harmony search algorithm with he KH algorithm to generate a new probability factor and improved exploration search ability, so as to ge<sup>t</sup> a better solution. Another algorithm is proposed by Abualigah et al. [67], which combined the objective function with the KH algorithm, and had better performance in solving the problem of text clustering. Niu et al. [68] sped up the exploration convergence and improved the efficiency by using the opposite learning strategy, using a sinusoidal graph to change the inertia weight, and modifying the search process according to the inertia weight and acceleration coefficient.
