Differential adaptive SA-DEPSO algorithm based on particle swarm

Abstract

The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.