Chaotic Particle Swarm Optimization Algorithm

This article explores Particle Swarm Optimization (PSO) and Chaotic Particle Swarm Optimization (CPSO) algorithms, both belonging to the category of intelligent optimization techniques. PSO is a nature-inspired algorithm that simulates collective behaviors like bird flocking or fish schooling to solve optimization problems. The core mechanism involves simulating particles moving through the solution space to locate optimal solutions, where each particle's position update follows velocity and position formulas typically implemented as: position[i] = position[i] + velocity[i] velocity[i] = w*velocity[i] + c1*rand()*(pbest[i] - position[i]) + c2*rand()*(gbest - position[i]) Chaotic Particle Swarm Optimization enhances standard PSO by incorporating chaotic search strategies, which utilize chaotic maps (such as logistic maps or tent maps) to generate pseudo-random sequences that improve global exploration capabilities. This hybrid approach achieves more comprehensive search coverage and higher convergence efficiency by alternating between chaotic local search and standard PSO operations, making it particularly effective for complex multimodal optimization problems.