Improved particle swarm optimization algorithm with random mutation and perception

Abstract

Since traditional particle swarm optimization(PSO) is prone to premature phenomenon when solving complex functions in high-dimensional space, a particle swarm optimization algorithm with random variation and dynamic perception factors in terms of the movement laws and dispersion characteristics of particles in space is proposed. In order to encourage individual particles to explore their own neighborhoods and reduce the premature phenomenon of particles due to over-reliance on individual optimality and global optimality, a random mutation factor with a questioning strategy for neighborhoods is added to the basic algorithm to improve the speed update. At the same time, a perception factor is added to the particle position update, so that the particle can dynamically and adaptively control the spatial distance between itself and other particles in the same dimension, so as to avoid falling into local optimum. The algorithm has obvious superiority and robustness in solving complex functions in high-dimensional space through test function experiments, algorithm comparison analysis experiments, random parameter influence experiments and algorithm complexity experiments.