A Physarum Network Evolution Model Based on IBTM

详细信息    查看全文

• 作者：Yuxin Liu (19)
  Zili Zhang (19) (20)
  Chao Gao (19)
  Yuheng Wu (19)
  Tao Qian (19)
  
• 关键词：Physarum Polycephalum ; Physarum Model ; IBTM ; Network Evolution
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7929
• 期：1
• 页码：27-34
• 全文大小：1123KB
• 参考文献：1. Nakagaki, T., Yamada, H., Hara, M.: Smart Network Solutions in an Amoeboid Organism. Biophysical Chemistry聽107(1), 1鈥? (2004)
  2. Nakagaki, T., Kobayashi, R., Nishiura, Y., Ueda, T.: Obtaining Multiple Separate Food Sources: Behavioural Intelligence in the Physarum Plasmodium. Proceedings of the Royal Society of London. Series B: Biological Sciences聽271(1554), 2305鈥?310 (2004)
  3. Adamatzky, A., Jones, J.: Road Planning with Slime Mould: If Physarum Built Motorways It Would Route M6/M74 through Newcastle. International Journal of Bifurcation and Chaos聽20(10), 3065鈥?084 (2010)
  4. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for Biologically Inspired Adaptive Network Design. Science Signalling聽327(5964), 439 (2010)
  5. Adamatzky, A.: Route 20, Autobahn 7 and Physarum Polycephalum: Approximating Longest Roads in USA and Germany with Slime Mould on 3D Terrains. arXiv preprint arXiv:1211.0519 (2012)
  6. Jones, J.: Characteristics of Pattern Formation and Evolution in Approximations of Physarum Transport Networks. Artificial Life聽16(2), 127鈥?53 (2010)
  7. Nakagaki, T., Guy, R.D.: Intelligent Behaviors of Amoeboid Movement Based on Complex Dynamics of Soft Matter. Soft Matter聽4(1), 57鈥?7 (2007)
  8. Nakagaki, T., Yamada, H., Toth, A.: Maze-solving by an Amoeboid Organism. Nature聽407(6803), 470 (2000)
  9. Tero, A., Kobayashi, R., Nakagaki, T.: A Mathematical Model for Adaptive Transport Network in Path Finding by True Slime Mold. Journal of Theoretical Biology聽244(4), 553鈥?64 (2007)
  10. Adamatzky, A.: Physarum Machines: Computers from Slime Mould. World Scientific Publishing Company Incorporated (2010)
  11. Adamatzky, A.: Physarum Machines: Encapsulating Reaction-diffusion to Compute Spanning Tree. Naturwissenschaften聽94(12), 975鈥?80 (2007)
  12. Jones, J.: The Emergence and Dynamical Evolution of Complex Transport Networks from Simple Low-level Behaviours. International Journal of Unconventional Computing聽6(2), 125鈥?44 (2010)
  13. Gunji, Y.P., Shirakawa, T., Niizato, T., Haruna, T.: Minimal Model of a Cell Connecting Amoebic Motion and Adaptive Transport Networks. Journal of Theoretical Biology聽253(4), 659鈥?67 (2008)
  14. Niizato, T., Shirakawa, T., Gunji, Y.P.: A Model of Network Formation by Physarum Plasmodium: Interplay between Cell Mobility and Morphogenesis. Biosystems聽100(2), 108鈥?12 (2010)
  15. Gunji, Y.P., Shirakawa, T., Niizato, T., Yamachiyo, M., Tani, I.: An Adaptive and Robust Biological Network Based on the Vacant-particle Transportation Model. Journal of Theoretical Biology聽272(1), 187 (2011)
  16. Tero, A., Kobayashi, R., Nakagaki, T.: Physarum Solver: A Biologically Inspired Method of Road-network Navigation. Physica A: Statistical Mechanics and Its Applications聽363(1), 115鈥?19 (2006)
  
• 作者单位：Yuxin Liu (19)
  Zili Zhang (19) (20)
  Chao Gao (19)
  Yuheng Wu (19)
  Tao Qian (19)
  
  19. School of Computer and Information Science, Southwest University, Chongqing, 400715, China
  20. School of Information Technology, Deakin University, VIC, 3217, Australia
  
• ISSN：1611-3349

文摘

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 new areas. 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 new areas, and deploys multiple 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.