Simulated Annealing-Based Particle Swarm Optimization Algorithm

The simulated annealing-based particle swarm optimization algorithm is designed for solving extremum problems in complex functions. This hybrid approach combines the global exploration capabilities of particle swarm optimization with the local refinement mechanism of simulated annealing, enabling more effective search for optimal solutions. In this implementation, the algorithm maintains a population of particles that explore the solution space while incorporating a temperature-controlled acceptance criterion from simulated annealing. This helps avoid premature convergence and enhances the ability to escape local optima. The example program includes three benchmark test functions: - Camel function: A multi-modal function with multiple local minima, challenging for optimization algorithms - Rastrigrin function: A highly multimodal function characterized by frequent local minima - Ackley function: A complex function with a nearly flat outer region and a large hole at the center Key implementation features include: - Adaptive temperature scheduling that decreases gradually according to a cooling schedule - Particle velocity and position updates with simulated annealing-based acceptance probability - Fitness evaluation for each particle at every iteration - Convergence criteria based on either maximum iterations or solution quality thresholds Through this algorithm, we can effectively optimize these benchmark functions and identify their extremum points with improved accuracy and convergence properties compared to standard PSO implementations.