Particle swarm optimization based on simulated annealing rules
Mengao Yu, Zhong Chen, Linzhi Ding, Haoyu Cheng · 2023
Aiming at the problems of traditional particle swarm optimization (PSO) algorithm such as slow convergence speed and easy to fall into local optimum, this paper proposes to use the core idea of simulated annealing algorithm to improve the particle swarm optimization algorithm. First, the Metropolis criterion is used to select the probability of inertia weight, and for each particle in each iteration, the probability of inertia weight is selected to balance the search ability of particles; Secondly, by analyzing the information exchange of the optimal solution between each iteration, the mutation particles are constructed, and the position information of the next generation of global learning particles is determined through probability selection, which can effectively prevent the particle population from falling into the local optimal region. The simulation results show that the PSO algorithm based on simulated annealing rules has higher convergence accuracy and speed compared with other test algorithms, and can effectively improve the ability of PSO to find the optimal solution.