Simulated Annealing-Based Particle Swarm Optimization Algorithm

This article focuses on the simulated annealing-based particle swarm optimization algorithm. The algorithm demonstrates strong probabilistic jumping capabilities during the search process, implemented through a Metropolis acceptance criterion where worse solutions are occasionally accepted based on a temperature parameter that decreases over iterations. This mechanism effectively prevents the search from becoming trapped in local optima, a crucial feature for maintaining global optimization performance. The algorithm's implementation typically involves maintaining particle velocity and position updates from standard PSO, while incorporating simulated annealing's cooling schedule and probability evaluation. This dual approach enables more extensive exploration of the solution space, significantly improving both search efficiency and solution accuracy through balanced exploitation-exploration dynamics. Key implementation aspects include: - Temperature initialization and cooling schedule management - Fitness evaluation with probability-based acceptance rules - Adaptive inertia weight adjustment combining PSO and SA characteristics - Termination conditions based on temperature thresholds and convergence metrics Due to these enhanced capabilities, the simulated annealing-based particle swarm optimization algorithm finds widespread applications in various fields including image processing (for optimization tasks like segmentation), data mining (for feature selection and clustering), and machine learning (for hyperparameter tuning and model optimization).