期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2016
卷号：6
期号：5
页码：2470-2477
DOI：10.11591/ijece.v6i5.pp2470-2477
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：In particle swarm optimization, a set of particles move towards the global optimum point according to their experience and experience of other particles. Parameters such as particle rate, particle best experience, the best experience of all the particles and particle current position are used to determine the next position of each particle. Certain relationships received the input parameters and determined the next position of each particle. In this article, the relationships are accurately assessed and the amount of the effect of input parameters is horizontally set. To set coefficients adaptively, the notion is taken from bee behavior in collecting nectar. This method was implemented on software and examined in the standard search environments. The obtained results indicate the efficiency of this method in increasing the rate of convergence of particles towards the global optimum.
其他摘要：In particle swarm optimization, a set of particles move towards the global optimum point according to their experience and experience of other particles. Parameters such as particle rate, particle best experience, the best experience of all the particles and particle current position are used to determine the next position of each particle. Certain relationships received the input parameters and determined the next position of each particle. In this article, the relationships are accurately assessed and the amount of the effect of input parameters is horizontally set. To set coefficients adaptively, the notion is taken from bee behavior in collecting nectar. This method was implemented on software and examined in the standard search environments. The obtained results indicate the efficiency of this method in increasing the rate of convergence of particles towards the global optimum.
关键词：Computer and Informatics;particle swarm optimization; global optimum; adaptive setting; the rate of convergence of particles; standard search environment
