Research on Improved Particle Swarm Optimization Algorithm Based on Simulated Annealing Algorithm
Wu Qingling, Jin Yubo · 2023
Early local optima convergence and poor convergence pace in subsequent iterations are issues with the particle swarm optimization algorithm. The probabilistic jumping capability of the simulated annealing algorithm effectively prevents the search from entering local optima. In order to prevent premature convergence to local optima, this work offers an enhanced particle swarm optimization approach that combines a simulated annealing algorithm with probability-based decision-making to manage the particle's direction and velocity in updates. The efficiency of the modified particle swarm optimization approach is assessed using three test functions.