Research On Population Topologies Of Particle Swarm Optimization And Its Variants

Particle swarm optimization algorithm is a swarm intelligence optimization technology proposed by Kennedy and Eberhart in 1995.After more than 20 years of research and development,the particle swarm optimization algorithm has received extensive attention from researchers in various fields and has become a research hotspot in the field of swarm intelligence optimization.The particle swarm optimization algorithm has been widely used in many scientific and engineering fields and achieved great success due to its simple concept,easy implementation,and fast convergence speed.However,the population topology research of particle swarm optimization algorithm and its variant algorithm is relatively immature.This dissertation studies the effect of population topology on particle swarm optimization and its variants from the perspective of information propagation.The main work includes: first,based on the analysis of the particle swarm optimization algorithm,a method for measuring the speed of information propagation in the population is proposed,and compared with other topology measures,the results show that the information propagation speed is a topological measure that can correctly reflect the performance of particle swarm optimization algorithm.Second,the effect of population topology on particle swarm optimization algorithm is studied.Studies have shown that information propagation speed is highly negatively correlated with population diversity.Slow information propagation makes particle swarm optimization algorithms have strong exploration capabilities,while fast information propagation is particle swarm optimization algorithms with strong development capabilities.Then,the relationship between the information propagation speed and the optimization result is studied.The results show that when optimizing the unimodal separable test function,the population topology with fast information propagation speed has advantages.When optimizing the two types of multimodal non-separable and hybrid test functions,the population topology with slow information propagation speed has advantages.Third,the influence of population topology on the four particle swarm variants is studied.Studies have shown that the speed of information propagation is also highly negatively correlated with the diversity of variant algorithms.Similarly,when optimizing unimodal separable test functions,population topologies with fast information propagation speed have advantages,and when optimizing multimodal non-separable and hybrid test functions,population topologies with slow information propagation speed have advantages.Fourth,to provide recommendations for population topology selection of particle swarm optimization and its variants.After testing 10 population topologies,combined with the third and fourth conclusions,it provides recommendations for the population topology selection of particle swarm optimization and its variants.The third and fourth research results show that the population topology has a certain similarity to the performance of particle swarm optimization and particle swarm optimization algorithms.Therefore,the proposal of population topology selection is likely to be applicable to other particle swarm variant algorithms,and may even be applicable to new particle swarm variant algorithms proposed in the future.The paper studies the correlation between the information propagation speed and the performance of particle swarm optimization and its variant algorithms,and provides suggestions for the topology selection of PSO and its variant algorithms.The results show that the information propagation speed is negatively correlated with the population diversity,and the slow information propagation makes the particle swarm optimization algorithm have strong exploration ability,which makes the algorithm have better ability to solve difficult problems.