Hybrid Algorithm Combining Simulated Annealing with Particle Swarm Optimization

By integrating Simulated Annealing (SA) with Particle Swarm Optimization (PSO), we develop a hybrid algorithm implementation that not only functions effectively but demonstrates rapid convergence rates. This combined approach leverages SA's ability to escape local optima through temperature-controlled probability acceptance, while PSO efficiently explores the search space using social particle interactions. The algorithm typically implements a dual-phase structure where PSO handles global exploration and SA performs local refinement, utilizing fitness evaluation functions and dynamic parameter adjustment. Key implementation aspects include velocity-position updates for particle movement and Boltzmann probability criteria for solution acceptance. This hybrid method significantly improves optimization efficiency by maintaining solution diversity while accelerating convergence toward global optima, making it particularly effective for complex multidimensional optimization problems.