由式（２）可以看出，在迭代过程中，系数Ａ的值随距离控制参数ａ的变化而不

It can be seen from equation (2) that during the iterative process, the value of coefficient A changes continuously with the change of distance control parameter a. In other words, the setting of the distance control parameter a affects the balance between the exploration and development capabilities of the GWO. However, from equation (4), the distance control parameter a linearly decreases from 2 to 0 as the number of iterations increases. The larger value of the distance control parameter a at the beginning of the iteration makes the search step size larger, and the exploration ability is stronger, avoiding premature convergence of the algorithm; the smaller a value at the later stage of the iteration makes the group focus on a certain area to search, and the development ability is stronger To speed up convergence. However, in the actual optimization problem, because the algorithm search process is extremely complicated, the linear decreasing strategy of the control parameter a is difficult to adapt to the actual search situation. Literature [1] The simulation results show that GWO is prone to fall into local optimum when solving multi-peak problems

It can be seen from the formula (2) that the value of coefficient A changes with the change of distance control parameter a during the iteration. In other words, the setting of distance control parameter a affects the balance between GWO's exploration and development capabilities. However, by formula (4), the distance control parameter a decreases from 2 linear to 0. The larger distance control parameter a value in the early part of the iteration makes the search step is larger, the exploration ability is strong, and the algorithm avoids the precocious convergence, and the smaller a value in the later iteration makes the group concentrate in a certain area search, the development ability is strong, and the convergence speed is accelerated. However, in the actual optimization problem, because the algorithm search process is extremely complex, it is difficult to adapt the control parameter a linear decreasing strategy to the actual situation of search. The simulation results of the literature show that GWO is prone to local optimality in solving the problem of multi-peak

It can be seen from equation (2) that the value of coefficient a changes with the change of distance control parameter A. in other words, the setting of distance control parameter a affects the balance between GWO's exploration and development ability. However, it can be seen from equation (4) that distance control parameter a decreases linearly from 2 to 0 as the number of iterations increases The a value of distance control parameter makes the search step larger and the search ability stronger, so as to avoid premature convergence of the algorithm; the smaller a value in the later iteration period makes the group concentrate on a certain area to search, so the development ability is stronger and the convergence speed is faster. However, in practical optimization problems, because the search process of the algorithm is very complex, the linear decline strategy of control parameter a is difficult to adapt to the search reality The simulation results in reference [1] show that GWO is easy to fall into local optimum when solving multi peak problem<br>