Particle Swarm Optimization Algorithm Based on Simulated Annealing

The particle swarm optimization algorithm based on simulated annealing generates a new position through small-scale random perturbations and calculates fitness values for both old and new positions. Following the Metropolis acceptance criterion of simulated annealing, it accepts the new position as the optimal solution. Experimental results demonstrate that this method significantly improves optimization performance, accelerates convergence speed, and enhances inversion efficiency. Implementation typically involves combining PSO's velocity-position update mechanism with SA's probabilistic acceptance strategy through temperature-controlled probability functions.