In order to solve the problem of wireless sensor network (WSN) coverage optimization

an adaptive fruit fly optimization algorithm was proposed. The algorithm increases the self-adaptive ability on the basis of the original iterative step operator

and proposes the method of reducing the dimension and increasing the step size to improve the convergence accuracy for the case where the subsequent optimal solution does not change for several consecutive generations. With strong global optimization performance

it is not easy to fall into local optimum

and the layout optimization of the WSN network node can be implemented quickly and efficiently

resulting in higher network coverage. Through the comparison of simulation experiments

it is proved that the adaptive fruit fly optimization algorithm proposed in this paper is not only better than the original fruit fly algorithm in the optimization performance

but also has certain advantages compared with other improved algorithms.