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A new internet of things group search optimizer
Author(s) -
Feng Xiang,
Liu Xiaoting,
Yu Huiqun
Publication year - 2016
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2891
Subject(s) - computer science , the internet , particle swarm optimization , group (periodic table) , artificial intelligence , machine learning , world wide web , chemistry , organic chemistry
Summary The development of the Internet of Things brings new opportunities and challenges for sensor networks. The scale of sensor networks tends to be larger. And the fusion rules need to be intelligent. In this paper, we propose a new Internet of Things group search optimizer (ITGSO) to solve intelligent information fusion problems in the high‐dimensional multi‐sensor networks. ITGSO is inspired by the latest research achievement about leader decision in Nature and works about social coordination, which mainly consists of three parts: basic group search optimizer , binary group search optimizer , and social decision model . With ITGSO, we need less time to obtain minimum Bayes cost than particle swarm optimization. And information of uncertain social intelligent problems can be fused. In this paper, we give the theoretical basic of ITGSO and proved its validity via mathematical analysis and simulation results. Copyright © 2014 John Wiley & Sons, Ltd.