Adaptive Adjustment of PSO Coefficients Taking the Notion from the Bee Behavior in Collecting Nectar

Abbas Fadavi, Karim Faez, Zeinab Famili


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.


particle swarm optimization, global optimum, adaptive setting, the rate of convergence of particles, standard search environment

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).