Dominating Global Best Selection for Multi-objective Particle Swarm Optimization

DOI

10.2991/iccsee.2013.378How to use a DOI?

Keywords

multi-objective optimization problem, particle swarm optimization, global best, pareto dominance, archive

Abstract

For multi-objective particle swarm optimization, the selection of the global best becomes an interesting topic, because it balances convergence and diversity. But the global best selected by the existing strategies has a high probability of not dominating the particle. The flight towards the global best not dominating the particle is expected to cause some objectives to become worse, thus does not surely promote convergence. As the accumulation of the flights, the algorithm suffers from slow convergence. Therefore we propose the dominating strategy to accelerate the convergence by decreasing that probability. Experimental results show our strategy outperforms other strategies.

Copyright

© 2013, 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  - CONF
AU  - Heming Xu
AU  - Yinglin Wang
AU  - Xin Xu
PY  - 2013/03
DA  - 2013/03
TI  - Dominating Global Best Selection for Multi-objective Particle Swarm Optimization
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1503
EP  - 1506
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.378
DO  - 10.2991/iccsee.2013.378
ID  - Xu2013/03
ER  -