A novel hybrid particle swarm optimization using adaptive strategy

Particle swarm optimization (PSO) has been employed to solve numerous real-world problems because of its strong optimization ability and easy implementation. However, PSO still has some shortcomings in solving complicated optimization problems, such as premature convergence and poor balance between global exploration and local exploitation. A novel hybrid particle swarm optimization using adaptive strategy (ASPSO) is developed to address associated difficulties. The contribution of ASPSO is threefold: (1) a chaotic map and an adaptive position updating strategy to balance exploration behavior and exploitation nature in the search progress; (2) elite and dimensional learning strategies to enhance the diversity of the population effectively; (3) a competitive substitution mechanism to improve the accuracy of solutions. Based on various functions from CEC 2017, the numerical experiment results demonstrate that ASPSO is significantly better than the other 16 optimization algorithms. Furthermore, we apply ASPSO to a typical industrial problem, the optimization of melt spinning progress, where the results indicate that ASPSO performs better than other algorithms.