An Improved Particle Swarm Optimization Algorithm Based on Entropy and Fitness of Particles
Huafen Yang, Yong Li, Lihua Yang, Qian Wu · 2020
Particle swarm optimization (PSO) is an algorithmic technique for optimization by solving a wide range of optimization problem. This paper presents an improved PSO. The proposed algorithm consists of two parts. Firstly, population initialization method based on entropy is proposed. Secondly, an improved accelerated learning coefficient is proposed. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity and the acceleration coefficient is relatively decreased. The entropy of each dimension is calculated to decrease the randomness of swarm and increase population diversity. Each particle has a different learning rate according to its fitness during evolution, which can balance the global and local searching ability of the population and avoid falling into local optimum. Experimental results show that, the proposed algorithm remarkably improves the ability of PSO to jump out of the local optima and significantly enhance the convergence precision.