Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm inspired by the social behavior of bird flocking, aiming to find optimal solutions within complex search spaces. Current research focuses on hybridizing PSO with other techniques, such as neural networks, fuzzy logic, and other metaheuristics (e.g., genetic algorithms, ant colony optimization), to improve performance and address limitations like premature convergence. These advancements are impacting diverse fields, including engineering design (e.g., microstrip coupler optimization, robotic arm kinematics), environmental monitoring (e.g., UAV path planning, PM2.5 forecasting), and data analysis (e.g., emotion recognition from EEG signals, maximum likelihood estimation).