Particle Swarm Optimization Variant

Particle swarm optimization (PSO) variants are actively being developed to improve the efficiency and robustness of this popular optimization algorithm. Current research focuses on enhancing convergence speed and global search capabilities through techniques like targeted mutation, elitism strategies, and adaptive parameter control using reinforcement learning. These advancements aim to address PSO's limitations, particularly slow convergence and susceptibility to local optima, leading to improved performance in diverse applications, including training physics-informed neural networks and solving complex optimization problems. The resulting algorithms show significant improvements over standard PSO in benchmark tests, demonstrating the value of these ongoing refinements.