关键词: 粒子群算法, 粒子质量, 运动学原理, 梯度场, 电磁学, 动量守恒定理

Abstract: To solve the problem that once Particle Swarm Optimization（PSO） algorithms find a local optimization it is hard for them to jump out and continue a global optimization，an improved particle swarm optimization which considers the quality of the particle is proposed.This algorithm is inspired by the kinematic theory.The particle is updated not only by the best previous position and the best position among all the particles in the swarm，but also by the role of the gradient field which is caused by the particle mass.When the particle swarm appears premature convergence，law of conservation of momentum is used to update the best previous position so that groups jump out from the local optimum area in time.Simulation results show that，compared with other improved PSO algorithms proposed before，it improves both optimization precision and efficiency when the improved PSO algorithm is used to optimize 4 typical benchmarks.

Key words: Particle Swarm Optimization（PSO）, particle mass, kinematic theory, gradient field, electromagnetism, law of conservation of momentum