Zhan, Z.-H., Zhang, J., Li, Y., and Chung, H.-S.-H. (2009) propose Adaptive Particle Swarm Optimization (APSO), an enhanced version of the classical Particle Swarm Optimization (PSO) that improves search efficiency and convergence speed. APSO dynamically adjusts key parameters like inertia weight and acceleration coefficients based on the evolutionary state of the swarm, which is determined by analyzing population distribution and fitness. It also incorporates an elitist learning strategy to help escape local optima during convergence. The algorithm is evaluated on 12 benchmark functions, showing significant improvements in convergence speed, global optimality, and solution accuracy compared to traditional PSO. APSO introduces only two new parameters, avoiding additional complexity. The study highlights the importance of adaptive parameter control and evolutionary state detection in enhancing PSO performance. The results demonstrate that APSO outperforms existing PSO variants in terms of efficiency and effectiveness, particularly in complex, multimodal optimization problems.Zhan, Z.-H., Zhang, J., Li, Y., and Chung, H.-S.-H. (2009) propose Adaptive Particle Swarm Optimization (APSO), an enhanced version of the classical Particle Swarm Optimization (PSO) that improves search efficiency and convergence speed. APSO dynamically adjusts key parameters like inertia weight and acceleration coefficients based on the evolutionary state of the swarm, which is determined by analyzing population distribution and fitness. It also incorporates an elitist learning strategy to help escape local optima during convergence. The algorithm is evaluated on 12 benchmark functions, showing significant improvements in convergence speed, global optimality, and solution accuracy compared to traditional PSO. APSO introduces only two new parameters, avoiding additional complexity. The study highlights the importance of adaptive parameter control and evolutionary state detection in enhancing PSO performance. The results demonstrate that APSO outperforms existing PSO variants in terms of efficiency and effectiveness, particularly in complex, multimodal optimization problems.