Improved Particle Swarm Optimization Algorithm Based on Dynamic Change Speed Attenuation Factor and Inertia Weight Factor

Compared with other optimization algorithms, the particle swarm optimization algorithm (pso) has the advantages of fast convergence, simple calculation, and fewer parameters to be adjusted. Therefore, it has been greatly developed in the past period of time. In order to further accelerate convergence, in recent years, more efficient and convenient particle swarm optimization algorithms such as LDWPSO, NLPSO, and DPSO have been proposed.However, these algorithms have many problems such as large amount of calculation, lack of precision, and easy to fall into local optimum.Therefore, the algorithm still needs to be improved. In order to obtain an efficient global search ability early in the algorithm iteration and better local optimization performance in the later stage of the algorithm iteration, this paper proposes a special expression form of dynamic change speed attenuation factor and inertia weight factor, and in the algorithm some random parameters are added to the iterative formula and applied to the algorithm iteration formula to improve the optimization speed and optimization effect of the algorithm.Finally, the effectiveness and correctness of the algorithm are also demonstrated by computer simulation experiments.