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An improved artificial fish cluster behavioral model and its simulation

Citation

Yuan, H and Huang, S and Chen, Y, An improved artificial fish cluster behavioral model and its simulation, Applied Mechanics and Materials, 433-435 pp. 770-773. ISSN 1660-9336 (2013) [Refereed Article]

Copyright Statement

Copyright 2013 Trans Tech Publications

DOI: doi:10.4028/www.scientific.net/AMM.433-435.770

Abstract

Biological cluster behavioral models rarely discuss about group cohesion issues at present, which makes biological colony susceptible to external environmental factors and leads to the group readily partitioning into multiple small groups. In order to enhance group cohesion and improve colony effect, the paper takes artificial fish as the research object and proposes a new model on artificial fish cluster behavior. This model is improved from standard self-propelled particles model, utilizing topological distance to ensure surrounding neighbors and to restrain the cognitive level of artificial fish. Meanwhile, Sphere method based on bounding box is adopted to test collision and proposes a hybrid collision processing scheme that only applies to cluster behavior, aimed at avoiding the penetration phenomenon of artificial fish. Finally, Experiments have been conducted. Results show that this model can simulate the cluster behavior of artificial fish precisely and make the group cohesion stronger. The proposed method is a feasible solution to the cluster model problem of cohesion.

Item Details

Item Type:Refereed Article
Keywords:artificial fish, cluster behavioral model, group cohesion, cognitive constraint, collision detection
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Life
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Chen, Y (Ms Ying Chen)
ID Code:88353
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2014-01-29
Last Modified:2017-11-18
Downloads:0

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