Annealed Particle Swarm Optimization Algorithm

Original: Optimization of Particle Swarm Using Simulated Annealing Principles

Annealed Particle Swarm Optimization (APSO) is an efficient optimization algorithm that integrates the characteristics of both Simulated Annealing (SA) and Particle Swarm Optimization (PSO). The core concept involves introducing the temperature parameter from simulated annealing into the particle state update mechanism of particle swarm optimization to enhance global search capabilities. In APSO, each particle represents a potential solution, and through collaborative interactions and competitive behavior among particles, the algorithm progressively converges toward optimal solutions. This hybrid approach typically implements a temperature-controlled acceptance probability for new solutions, where particles may accept worse solutions at higher temperatures to escape local optima. The algorithm finds extensive applications across engineering, physics, and computer science domains. Understanding and mastering APSO is crucial for deepening our comprehension and practical implementation of advanced optimization techniques, particularly when dealing with complex multimodal optimization problems where standard PSO tends to premature convergence.