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 distance control <br>system parameter is provided a balance between impact GWO exploration and development ability. However, by the formula (4), the parameters of a distance control iterations with the <br>increase in the number decreases from 0 to 2 linear. 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 <br>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 <br>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. [1] simulation <br>true results show, GWO into local optimum in addressing the problem of multimodal

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 distance control<br>The setting of parameter a affects the balance between GWO's exploration and development capabilities. However, by formula (4), the distance control parametera follows the iteration<br>The increase in the number decreases from 2 linear to 0. At the beginning of the iteration, the larger distance control parameter a value makes the search step larger, the exploration ability is strong, and avoids the algorithm.<br>Precocious convergence occurs, and the smaller a value in the later iteration makes the group concentrated in a certain area of search, the development ability is strong, and the convergence speed is accelerated. However, in the<br>In the actual optimization problem, because the algorithm search process is extremely complex, the control parameter a linear reduction strategy is difficult to adapt to the actual situation of search. Documentary 1.<br>The true results show that GWO is prone to local optimality in solving multi-peak problems

It can be seen from equation (2) that in the iterative process, the value of coefficient a changes with the change of distance control parameter A. in other words, distance control<br>The setting of control parameter a affects the balance between GWO's exploration and development capability. However, from equation (4), it can be seen that the distance control parameter a is iterative<br>At the beginning of the iteration, the larger distance control parameter a makes the search step larger, the exploration ability stronger and the algorithm avoided<br>In the later stage of the iteration, the smaller a value makes the group concentrate on a certain area to search, which has stronger development ability and faster convergence speed<br>In practical optimization problems, because the search process of the algorithm is very complex, it is difficult for the linear decreasing strategy of control parameter a to adapt to the actual situation of search<br>The real results show that GWO is easy to fall into local optimum when solving the multi peak problem<br>