文献类型：期刊文献

英文题名：A nonlinear dynamic adapted Particle Swarm Optimization algorithm

作者：Wei, Qing[1]; Bao, Zhiguo[1]

第一作者：魏庆

通讯作者：Wei, Q.|[1048412e14d82b2]魏庆;

机构：[1] School of Computer and Information Engineering, Henan University of Economics and Law, 450002, Zhengzhou, China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]School of Computer and Information Engineering, Henan University of Economics and Law, 450002, Zhengzhou, China|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2012

卷号：7

期号：1

起止页码：414-419

外文期刊名：International Review on Computers and Software

收录：EI(收录号：20123115300942);Scopus(收录号：2-s2.0-84864375988)

语种：英文

外文关键词：Dynamics - Lyapunov methods - Velocity

摘要：It is known that the standard Particle Swarm Optimization (PSO) algorithm has many advantages in some problems that need high speed of convergence. However, there are also some cases shown that the PSO algorithm cannot reach the top optimal situation sometimes; therefore, it prevented us to obtain accuracy solution in the way of fine tuning. Via the analysis on the influence of average absolute value of velocity of all particles on search ability of the PSO, a kind of improved particle swarm optimization is proposed, which has the characteristic of nonlinear dynamic adaptive velocity variation. To represent the briskness of all the particles, the average absolute value of velocity is applied as an index in this proposed improved algorithm. To enhance the search ability in the multidimensional space, the average absolute value of velocity changes along with a given nonlinear variation of ideal velocity by feedback control for tuning inertia weight. Experimental results represents that the convergence precision and the ability of PSO to escape the local optima is highly improved in the proposed algorithm. ? 2012 Praise Worthy Prize S.r.l. - All right reserved.