A decoupled adaptive particle swarm optimization method based on the large-scale optimization

Abstract:
PSO (Particle Swarm Optimization) is a meta heuristic algorithm, but it is not particularly ideal in large scale optimization. It can hardly balance the two contents of exploration and development effectively. In order to solve this issue, this article proposed an improved structure, which can decouple above two contents. It will help to explore and exploit the contents of different components independently at the same time. On this basis, a method of sparse particle distribution and local congestion estimation is proposed. This method can adjust the difference between samples and update particles in optimization process by using adaptive subgroup size adjustment. In order to verify the feasibility of this decoupled adaptive particle swarm optimization algorithm, this article preliminarily proves the convergence of the algorithm through theoretical analysis. In terms of experiments, comprehensive experiments are carried out on the method based on the largescale optimization benchmark data of CEC 2010 and CEC 2013. The results prove the effectiveness of the proposed strategy.