An improved particle swarm optimizer with shuffled sub-swarms and its application in soft-sensor of gasoline endpoint

DOI

10.2991/iske.2007.79How to use a DOI?

Keywords

Particle Swarm Optimizer; sub-swarm; shuffled; gasoline endpoint; soft-sensor

Abstract

This paper proposes a shuffled sub-swarms particle optimizer algorithm (SSPSO) to enhance the diversity of particles in the swarm to improve the performance of PSO. SSPSO is tested with a series of benchmark functions and compared with other version PSO algorithms. Experimental results show that SSPSO improves the search performance on the benchmark functions significantly. Furthermore, SSPSO is used to train NN to construct an artificial neural network SSPSONN. Then SSPSONN is applied to construct a soft-sensor of gasoline endpoint and compared with actual industrial data, the results show that the constructed soft-sensor is feasible and effective.

Copyright

© 2007, 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  - Hui Wang
AU  - Feng Qian
PY  - 2007/10
DA  - 2007/10
TI  - An improved particle swarm optimizer with shuffled sub-swarms and its application in soft-sensor of gasoline endpoint
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 468
EP  - 473
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.79
DO  - 10.2991/iske.2007.79
ID  - Wang2007/10
ER  -