An improved convergence particle swarm optimization algorithm with random sampling of control parameters (Q2303400)

Summary: Although particle swarm optimization (PSO) has been widely used to address various complicated engineering problems, it still needs to overcome the several shortcomings of PSO, e.g., premature convergence and low accuracy. Its final optimization result is related to the control parameters selection; therefore, an improved convergence particle swarm optimization algorithm with random sampling of control parameters is proposed. For the proposed algorithm, the random sampling strategy of control parameters is designed, which can promote the flexibility of algorithm parameters and simultaneously enhance the updating randomness for both particle velocity and position. According to the convergence analysis of PSO, the sampling range for inertial weight is determined after both the acceleration factors have already been sampled in their respective value interval, to ensure convergence for every evolution step of algorithm. Besides that, in order to make full use of dimension information of some better particles, the stochastic correction approach on each dimension for the population optimum value has been adopted. The final experiments results demonstrate that the proposed algorithm further improves the convergence rate while maintaining higher convergence accuracy, compared with basic particle swarm optimization and other variants.