Adaptive simulated annealing particle swarm optimization algorithm

Particle swarm optimization is widely used in various fields because of the few parameters to be set and the simple calculation structure. In order to improve the optimization speed and accuracy of the PSO, and to avoid falling into the local optimal solution, an adaptive simulated annealing PSO is proposed, which uses the hyperbolic tangent function to control the inertia weight factor for nonlinear adaptive changes, uses linear change strategies to control 2 learning factors, introduces the simulation annealing operation, set a temperature according to the initial state of the population, guide the population to accept the difference solution with a certain probability according to the Metropolis criterion, and ensure the ability to jump out of the local optimal solution. To verify the effect of the algorithm proposed in this paper, 7 typical test functions and 5 algorithms proposed in the literature are selected for comparison and testing. According to the average value, standard deviation and number of iterations of the optimization results, the algorithm proposed in this paper has greatly improved the iteration accuracy, convergence speed and stability so as to overcome the shortcomings of particle swarm optimization. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.