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Paper Abstract and Keywords
Presentation 2026-07-09 13:00
Comparison of Particle Swarm Optimization and Genetic Algorithm for Identifying Limit Cycles Across Stable and Unstable Parameter Regions
Kai Yamaguchi, Katsutoshi Yoshida, Yoshikazu Yamanaka (Utsunomiya Univ.) NLP2026-26
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we investigate a method for identifying the parameters of mathematical models of periodic motions observed in nonlinear autonomous systems from observational data. When unstable regions exist in the search parameter space, candidate solutions may diverge, which can significantly reduce the efficiency of parameter search. We demonstrate the extent of this deterioration using the results obtained by the proposed genetic algorithm and those obtained by the conventional particle swarm optimization method, and compare the performance of the two approaches.Numerical results show that the genetic
algorithm concentrates candidate solutions in the stable region earlier than particle swarm optimization. In the successful trials, the genetic algorithm reduced both the time and the number of function evaluations required to reach the target MSE. On the other hand, some trials stagnated, and the success rate of the genetic algorithm was lower than that of particle swarm optimization. These results suggest that the genetic algorithm can improve search efficiency by reducing the influence of unstable regions, while its search performance may vary depending on the trial.
Keyword (in Japanese) (See Japanese page) 
(in English) Genetic Algorithm / Particle Swarm Optimization / Limit Cycle / Parameter Identification / Parameter Identification / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 94, NLP2026-26, pp. 1-6, July 2026.
Paper # NLP2026-26 
Date of Issue 2026-07-02 (NLP) 
ISSN 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)
Download PDF NLP2026-26

Conference Information
Committee NLP  
Conference Date 2026-07-09 - 2026-07-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Toki Koryu Kaikan (Sado) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2026-07-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Comparison of Particle Swarm Optimization and Genetic Algorithm for Identifying Limit Cycles Across Stable and Unstable Parameter Regions 
Sub Title (in English)  
Keyword(1) Genetic Algorithm  
Keyword(2) Particle Swarm Optimization  
Keyword(3) Limit Cycle  
Keyword(4) Parameter Identification  
Keyword(5) Parameter Identification  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kai Yamaguchi  
1st Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
2nd Author's Name Katsutoshi Yoshida  
2nd Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
3rd Author's Name Yoshikazu Yamanaka  
3rd Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
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Speaker Author-1 
Date Time 2026-07-09 13:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2026-26 
Volume (vol) vol.126 
Number (no) no.94 
Page pp.1-6 
#Pages 6 
Date of Issue 2026-07-02 (NLP) 

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