Particle Swarm Optimization (PSO) - An Evolutionary Computation Technique

Particle Swarm Optimization (PSO) is an evolutionary computation technique co-invented by Dr. Eberhart and Dr. Kennedy in 1995. The algorithm originates from studies of bird flock predatory behavior, simulating information exchange and cooperation among individuals within a swarm to solve optimization problems. Similar to Genetic Algorithms, PSO is an iteration-based optimization tool that continuously adjusts particle positions and velocities to seek optimal solutions. The core implementation involves three key operations: velocity update using personal best (pbest) and global best (gbest) positions with inertia weight adjustment, position update through vector addition, and fitness evaluation at each iteration. PSO finds extensive applications across various fields including engineering, economics, and computer science. By employing PSO algorithms, researchers can effectively solve complex optimization problems while significantly improving solution efficiency through its parallel search characteristics and social behavior modeling.