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Paper Abstract and Keywords
Presentation 2018-01-27 13:35
The Search Feature of Particle Swarm Optimizer with Sensors in Dynamic Environment
Hiroshi Sho (KIT) NC2017-63
Abstract (in Japanese) (See Japanese page) 
(in English) In order to perform the search of particle swarm optimizer under dynamic environment, as a previous study, author has proposed three methods which are particle swarm optimizer with sensors (PSOS), particle swarm optimizer with inertia weight with sensors (PSOIWS) and canonical particle swarm optimizer with sensors (CPSOS), respectively. For realizing better search performance and efficiency by using these methods, in this paper, we combine the obtained search information and sensor information to determine the most position of the changed best solution (moving target during the search process. Then, based on the measured results, it is possible to track the moving target promptly. In our simulation experiments, with changing the parameters (number, sensing distance) of the sensors, we investigate the search feature of each method against the variable speed 8 type of tracking problems. Based on analyzing results, we reveal the outstanding tracking ability of PSOIWS and CPSOS methods.
Keyword (in Japanese) (See Japanese page) 
(in English) swarm intelligence / particle swarm optimization / sensor / tracking ability / cumulative fitness / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 417, NC2017-63, pp. 77-82, Jan. 2018.
Paper # NC2017-63 
Date of Issue 2018-01-19 (NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee MBE NC NLP  
Conference Date 2018-01-26 - 2018-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ME, generalImplementation of Neuro Computing,Analysis and Modeling of Human Science, 
Paper Information
Registration To NC 
Conference Code 2018-01-MBE-NC-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) The Search Feature of Particle Swarm Optimizer with Sensors in Dynamic Environment 
Sub Title (in English)  
Keyword(1) swarm intelligence  
Keyword(2) particle swarm optimization  
Keyword(3) sensor  
Keyword(4) tracking ability  
Keyword(5) cumulative fitness  
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1st Author's Name Hiroshi Sho  
1st Author's Affiliation Kyushu Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2018-01-27 13:35:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2017-63 
Volume (vol) vol.117 
Number (no) no.417 
Page pp.77-82 
#Pages
Date of Issue 2018-01-19 (NC) 


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