A physarum network evolution model based on IBTM

Liu, Yuxin, Zhang, Zili, Gao, Chao, Wu, Yuheng and Qian, Tao 2013, A physarum network evolution model based on IBTM, in Advances in swarm intelligence, Springer, Berlin, Germany, pp.19-26.

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Title A physarum network evolution model based on IBTM
Author(s) Liu, Yuxin
Zhang, Zili
Gao, Chao
Wu, Yuheng
Qian, Tao
Title of book Advances in swarm intelligence
Editor(s) Tan, Ying
Shi, Yuhui
Mo, Hongwei
Publication date 2013
Series Lecture Notes in Computer Science ; v.7929
Chapter number 3
Total chapters 63
Start page 19
End page 26
Total pages 8
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) IBTM
Network Evolution
Physarum Model
Physarum Polycephalum
Summary The traditional Cellular Automation-based Physarum model reveals the process of amoebic self-organized movement and self-adaptive network formation based on bubble transportation. However, a bubble in the traditional Physarum model often transports within active zones and has little change to explore newareas.And the efficiency of evolution is very low because there is only one bubble in the system. This paper proposes an improved model, named as Improved Bubble Transportation Model (IBTM). Our model adds a time label for each grid of environment in order to drive bubbles to explore newareas, and deploysmultiple bubbles in order to improve the evolving efficiency of Physarum network.We first evaluate the morphological characteristics of IBTM with the real Physarum, and then compare the evolving time between the traditional model and IBTM. The results show that IBTM can obtain higher efficiency and stability in the process of forming an adaptive network.
Notes This paper was presented at the International Conference on Advances in Swarm Intelligence (4th : 2013 : Harbin, China)
ISBN 3642387144
9783642387142
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060717

Document type: Book Chapter
Collection: School of Information Technology
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