Abstract: The particle swarm optimization (PSO) method can have difficulty reaching local minima and have difficulty optimizing high-dimensional nonlinear problems. In order to address these concerns, we propose an adaptive particle swarm optimization algorithm with local search. The core premise of the algorithm is to adjust the algorithm parameters dynamically based on the population distribution information and to increase population diversity by incorporating a chaos mutation mechanism. A mechanism is added to strengthen the local search ability of the algorithm. The optimization results of six benchmarking functions show that the algorithm exhibits better optimization performance. We also apply the algorithm to the optimization of two actual network cases: the Hanoi network and the New York network. The results show that the algorithm provides a better search precision and faster convergence speed than other algorithms.