Particle Swarm Optimization (PSO) - An Evolutionary Computation Technique
Particle Swarm Optimization (PSO) is an evolutionary computation technique invented by Dr. Eberhart and Dr. Kennedy, inspired by bird flock predatory behavior. Similar to genetic algorithms, PSO is an iterative optimization tool that initializes with random solutions and searches for optimal values through iterations. Unlike genetic algorithms, PSO eliminates crossover and mutation operations, instead having particles follow the optimal particle in solution space. Future chapters will detail implementation steps and comparisons with genetic algorithms. PSO's advantages include simplicity of implementation with minimal parameter tuning, currently applied extensively in function optimization and neural network training. Code implementations typically involve velocity updates and position adjustments using personal and global best values.