Particle Swarm Optimization (PSO) - An Evolutionary Computation Technique

The article mentions that Particle Swarm Optimization (PSO) is an evolutionary computation technique invented by Dr. Eberhart and Dr. Kennedy, originating from research on bird flock predatory behavior. Similar to genetic algorithms, PSO is an iterative optimization tool that initializes with a set of random solutions and searches for optimal values through iterative processes. However, unlike genetic algorithms, PSO does not use crossover or mutation operations. Instead, particles search by following the optimal particle in the solution space. Subsequent chapters will detail PSO's procedural steps and compare it with genetic algorithms. PSO's advantage lies in its straightforward implementation requiring minimal parameter adjustments. Currently, PSO is widely applied in function optimization, neural network training, fuzzy system control, and other application domains of genetic algorithms. From a coding perspective, PSO implementations typically maintain particle positions and velocities as vectors, updating them using formulas that incorporate cognitive (personal best) and social (global best) components. The algorithm's core loop involves evaluating fitness functions, updating personal/global bests, and adjusting particle velocities and positions through weighted random factors.