NDLI: An improved particle swarm optimization using best neighbor with worst particle and its application in soft-sensor of gasoline endpoint

Content Provider	IEEE Xplore Digital Library
Author	Hui Wang
Copyright Year	2009
Description	Author affiliation: Computer Science and Information Engineering, Shanghai Institute of Technology, 200235, China (Hui Wang)
Abstract	This paper proposes out a variation of particle swarm optimization with best neighbor and worst particle (BNWPPSO). In BNWPPSO, some particles will be constructed as new neighbors of each particle and the best one of them will have influence on the behavior of the particle. The update formula of position is modified also to balance the local search ability and global search ability more efficiency. The worst particle of the swarm will be re-randomized at every generation to prevent premature convergence of PSO. BNWPPSO is investigated by several benchmark problems, the results show that BNWPPSO performances better than traditional PSO. Furthermore, BNWPPSO is applied to train artificial neural network to construct a soft-sensor of gasoline endpoint of crude distillation unit. The results show that the model constructed by BNWPPSO is feasible and effective.
Starting Page	387
Ending Page	390
File Size	220777
Page Count	4
File Format	PDF
ISBN	9781424452040
DOI	10.1109/ICACIA.2009.5361073
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	best neighbor soft-sensor Artificial neural networks Cognition Application software History Particle swarm optimization Petroleum Equations Convergence Computer science Velocity control worst particle
Content Type	Text
Resource Type	Article

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