中文摘要：

在粒子群优化算法中,粒子如何合理地利用自身经验信息和群体共享信息的问题一直未能有效解决.针对这一问题,基于认知论的观点,对速度更新公式中的随机因子进行了分析,建立了粒子对自身经验信息和群体共享信息认知的内在联系,提出了相关性粒子群优化模型.该模型采用Copula函数去刻画随机因子间的相关结构,而不同的相关结构和相关性程度反映了粒子对自身经验信息和群体共享信息的利用策略的差异,同时给出了基于Gaussian Copula的相关性粒子群优化模型的实现方法.理论上给出了随机因子间相关程度与群体多样性的关系式,表明了当随机因子间正线性相关时有利于维持群体的多样性.证明了随机因子间相关程度与算法收敛性的关系,同时给出了相关性粒子群优化模型的收敛条件.仿真实验结果表明,随机因子间相关程度的水平设置对模型的优化性能有非常显著的影响,当粒子的自身经验信息和群体共享信息被同等利用时,模型表现出优良的整体性能.

英文摘要：

In the study of particle swarm optimization,propertly using the individual experience and social sharing information of particles has always been a problem.To solve this problem,this paper analyzes the random factors in updating the velocity eguation in the view of cognition and creates the intrinsic cognitive relation between individual experience and social sharing information.First,a correlative particle swarm optimization model is developed,which uses the Copula function to measure the dependence among random factors.In the new model,the different correlation structures and degrees of correlation between random factors can denote different strategies,which are used to process individual experience and social sharing experience.Meanwhile,this paper provides a flowchart of the correlative particle swarm optimization model,based on Gaussian Copula.Second,the relationship between the degrees of correlation and population diversity is presented,which shows that the random factors with positive linear correlation avail to maintain population diversity.Finally,the relationship between the degrees of correlation and convergence is analyzed and the convergence conditions of the correlative particle swarm optimization model are provided.Experimental simulations show that the correlation of random factors have a much greater influence on the performance of the new model,which can greatly improve convergence velocity and precision when the random factors are a completely positive linear correlation.