Research on Improved Adaptive Chaos Optimization Particle Swarm Optimization Algorithm

In view of the advantages and disadvantages of particle swarm optimization (PSO) algorithm, an improved adaptive chaos optimization particle swarm optimization (ACPSO) algorithm is proposed. The chaos system has the characteristics of ergodicity, randomness, and sensitivity to initial condition. In the ACPSO algorithm, an initialization strategy based on logistic chaos opposition-based learning is applied to diversify the initial individuals in the search space. When the algorithm enters into the local optimal, the chaos operation is introduced into the algorithm, which could help the algorithm to escape from the local optimum and improve the global search ability. Simulation results show that this method not only effectively improve the convergence speed of PSO algorithm, but also improve the accuracy of the algorithm. By comparison with the other latest algorithms the ACPSO algorithm has better global and local searching abilities, and is especially suitable for high dimensional function optimization.