A Chaos Particle Swarm Optimization based on Adaptive Inertia Weight

In this paper, the chaotic optimization algorithm is embedded in traditional PSO algorithm using randomness and ergodicity of the chaotic characteristic, and random numbers and a inertia weight factor in PSO algorithm is substituted respectively by chaotic variables and an adaptive inertia weight function, so the ACEPSO algorithm is proposed in order to overcome the drawbacks of CPSO algorithm that easily falls into local optimum. With the simulation experiments, the effectiveness of ACEPSO algorithm is verified by optimization tests with the complex multi-dimensional function. The simulation results verify that the ACEPSO algorithm is robust, high precision, and strong global convergence, and is a kind of practical and feasible optimization algorithm.