Particle Swarm Optimization (PSO) Algorithm

Particle Swarm Optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart in 1995, inspired by simulations of bird flock predatory behavior. Similar to genetic algorithms, PSO operates as an iterative optimization tool but distinguishes itself by leveraging "cooperation" and "competition" among swarm individuals. Particles dynamically adjust their behavior based on personal and collective flight experiences. PSO's key advantage lies in its straightforward implementation with minimal parameter tuning. It has been widely applied to function optimization, neural network training, fuzzy system control, and other domains traditionally addressed by genetic algorithms.