Particle Swarm Optimization Algorithm Based on Simulated Annealing

The fundamental concept of the simulated annealing-based particle swarm optimization algorithm involves generating a new position through localized random perturbations and comparing fitness values between original and new positions. Using the Metropolis acceptance criterion from simulated annealing algorithms, it probabilistically accepts the new position as the optimal solution. In practical applications, this hybrid algorithm efficiently optimizes various complex problems including power system optimization, machine learning, and signal processing. Comparative experiments reveal that the simulated annealing-enhanced PSO significantly improves optimization outcomes while accelerating convergence rates and boosting inversion efficiency. The algorithm typically implements this through a temperature parameter that decreases over iterations, controlling the acceptance probability of worse solutions to escape local optima. Consequently, this methodology demonstrates broad application potential across multiple domains, providing effective solutions for diverse optimization challenges.