Premium
Comparison of linear and classical velocity update rules in particle swarm optimization: notes on diversity
Author(s) -
Wilke Daniel N.,
Kok Schalk,
Groenwold Albert A.
Publication year - 2006
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.1867
Subject(s) - particle swarm optimization , randomness , implementation , convergence (economics) , swarm behaviour , mathematical optimization , mathematics , computer science , algorithm , premature convergence , statistics , economics , programming language , economic growth
In this study, we investigate the significance of diversity in the particle swarm optimization (PSO) algorithm. To do so, we study two different implementations of the PSO, being the so‐called linear and classical PSO formulations. While the behaviour of these two implementations is markedly different, they only differ in the formulation of the velocity update rule. In fact, the differences are merely due to subtle differences in the introduction of randomness into the algorithm. In this paper, we show that in algorithms employing the linear PSO velocity update rule, particle trajectories collapse to line searches in n ‐dimensional space. The classical formulation does not suffer this drawback. Instead, directional diverse stochastic search trajectories are retained, which in turn helps to alleviate premature convergence. The performance of the classical implementation is therefore superior for all test problems considered, due to the presence of adequate diversity. Copyright © 2006 John Wiley & Sons, Ltd.