A novel multi-objective particle swarm optimization with -means based global best selection strategy

DOI

10.1080/18756891.2013.805584How to use a DOI?

Keywords

Particle swarm optimization, Multi-objective optimization, -means algorithm, Global best, Symmetric mutation operator

Abstract

In this paper, a multi-objective particle swarm optimization algorithm with a new global best () selection strategy is proposed for dealing with multi-objective problems. In multi-objective particle swarm optimization, plays an important role in convergence and diversity of solutions. A -means algorithm and proportional distribution based approach is used to select from the archive for each particle of the population. A symmetric mutation operator is incorporated to enhance the exploratory capabilities. The proposed approach is validated using seven popular benchmark functions. The simulation results indicate that the proposed algorithm is highly competitive in terms of convergence and diversity in comparison with several state-of-the-art algorithms.

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  - Chenye Qiu
AU  - Chunlu Wang
AU  - Xingquan Zuo
PY  - 2013
DA  - 2013/09/01
TI  - A novel multi-objective particle swarm optimization with -means based global best selection strategy
JO  - International Journal of Computational Intelligence Systems
SP  - 822
EP  - 835
VL  - 6
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.805584
DO  - 10.1080/18756891.2013.805584
ID  - Qiu2013
ER  -