A new joint spectral radius analysis of random PSO algorithm

DOI

10.1080/18756891.2014.960291How to use a DOI?

Keywords

Particle swarm optimization, Convergence analysis, The joint spectral radius, Monte Carlo method

Abstract

The existing stability analysis of particle swarm optimization (PSO) algorithm is chiefly concluded by the assumption of constant transfer matrix or time-varying random transfer matrix. Firstly, one counterexample is provided to show that the existing convergence analysis is not possibly valid for PSO system involving random variables. Secondly, the joint spectral radius, mainly calculated by the maximum eigenvalue of the product of all asymmetric random transfer matrices, is introduced to analyze and discuss convergence condition and convergence rate from numerical viewpoint with the aid of Monte Carlo method. Numerical results show that there is one major discrepancy between some preview convergence results and our corresponding results, helping us to deeply understand the tradeoff between exploration ability and exploitation ability as well as providing certain guideline for parameter selection.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jun Liu
AU  - Hongbin Ma
AU  - Xuemei Ren
AU  - Tianyun Shi
AU  - Ping Li
PY  - 2014
DA  - 2014/12/01
TI  - A new joint spectral radius analysis of random PSO algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 1022
EP  - 1043
VL  - 7
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.960291
DO  - 10.1080/18756891.2014.960291
ID  - Liu2014
ER  -