Particle Swarm Optimization Algorithm Using Opposition-based Learning and Gaussian Disturbance
ZHU Degang
HONG Jian
ZHANG Jie
Abstract：Standard particle swarm optimization(PSO)has some shortcomings,such as slow convergence velocity and getting trapped in a local minima. In order to overcome the problems,a new improved PSO algorithm using opposition-based learning and Gaussian disturbance(OGPSO)is proposed in this paper. On the basis of particle self-learning,a particle's opposite position is cho?sen randomly,the current particle learns from this opposite position,and increasing the population diversity. Gaussian disturbance is put into in the global optimum positions,which can prevent falling into local minima. Simulation results show that compared with the traditional well-known algorithms such as FIPS,HPSO-TVAC,DMS-PSO,CLPSO,APSO,etc.,the algorithm has obvious advantages in both 30-dimension and 100-dimension testing functions,whether convergence accuracy or convergence speed.
Keywords：particle swarm optimizationopposition-based learningGaussian disturbance
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2993-2998 )