article

Chaotic dynamic characteristics in swarm intelligence

Authors:

Maurice ClercAuthors Info & Claims

Applied Soft Computing, Volume 7, Issue 3

Pages 1019 - 1026

https://doi.org/10.1016/j.asoc.2006.10.006

Published: 01 June 2007 Publication History

Abstract

Swarm intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. The intelligence emerges from a chaotic balance between individuality and sociality. The chaotic balances are a characteristic feature of the complex system. This paper investigates the chaotic dynamic characteristics in swarm intelligence. The swarm intelligent model namely the particle swarm (PS) is represented as an iterated function system (IFS). The dynamic trajectory of the particle is sensitive on the parameter values of IFS. The Lyapunov exponent and the correlation dimension are calculated and analyzed numerically for the dynamic system. Our research results illustrate that the performance of the swarm intelligent model depends on the sign of the maximum Lyapunov exponent. The particle swarm with a high maximum Lyapunov exponent usually achieves better performance, especially for multi-modal functions.

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Chakraborty SMali K(2024)A multilevel biomedical image thresholding approach using the chaotic modified cuckoo searchSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09283-628:6(5359-5436)Online publication date: 1-Mar-2024
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Mohanty SDash R(2022)Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07257-826:18(9395-9417)Online publication date: 1-Sep-2022
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Index Terms

Chaotic dynamic characteristics in swarm intelligence
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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cover image Applied Soft Computing

Applied Soft Computing Volume 7, Issue 3

June, 2007

513 pages

ISSN:1568-4946

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Copyright © Elsevier B.V. © 2006.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2007

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Mohanty SDash R(2022)A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor modelProgress in Artificial Intelligence10.1007/s13748-022-00289-z11:4(349-366)Online publication date: 1-Dec-2022
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