A novel elite guidance-based social learning particle swarm optimization algorithm

To improve the premature convergence and poor global search capability of the classical particle swarm algorithm(PSO), this paper proposes a novel elite guidance-based social learning particle swarm optimization (ESLPSO) algorithm. In the ESLPSO algorithm, a hierarchical topological search method is proposed. The method divides particles into optimal elite particles and other civilian particles according to their fitness performance, revolutionizing the update sample of the traditional population iterative search and enhancing the guidance of the whole population evolution information. In addition, an elite particle-guided social learning strategy is designed to better utilize the multidimensional information on population evolution by increasing the uncertainty of state superposition. On this basis, the extremum perturbation migration mechanism motivates the particles to experience new search paths and regions, increase population diversity and balance the population's exploration and exploitation in the search process. Moreover, the Cubic chaos initialization is employed to endow the initial particle population with a wide coverage in the search space. Finally, 12 benchmark test function sets covering unimodal, multimodal and rotated-multimodal functions are used to validate the performance of the proposed algorithm. The results on comparing the ESLPSO algorithm with other eight improved PSO algorithms show that the ESLPSO algorithm has excellent search performances in solving different types of functions, having efficient robustness and excellent solutions.