On evolving environment of 2D P colonies: ant colony simulation. (English) Zbl 1543.68122
Summary: P colonies are very simple membrane systems originally derived from the P systems. The 2D P colonies, as a version of P colonies with a two-dimensional environment, were introduced as a theoretical model of the multi-agent system for observing the behavior of a community of very simple agents living in a shared environment. Each agent is equipped with a set of programs consisting of a small number of simple rules. These programs allow the agent to act and move in the environment. Although, the 2D P colonies proved to be suitable for the simulations of various (not only) multi-agent systems, and natural phenomena, like the flash floods, there are phenomena which they are not able to simulate without some additional features or characteristics. One of the ways the agents can share the information is to use the stigmergy, which means to leave some special symbols in the environment. In this paper, we follow our previous research on the 2D P colony. We present a model of the 2D P colony with evolving environment, which allows us to simulate phenomena like the stigmergy, hence to simulate an ant colony.
MSC:
| 68Q07 | Biologically inspired models of computation (DNA computing, membrane computing, etc.) |
Keywords:
ant-colony simulation; 2D P-colony; P-systems; evolving environment; optimization; bio-inspired computationReferences:
| [1] | Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In: European Conference on Artificial Life; str 134-142. |
| [2] | Păun, G., Computing with membranes, Journal of Computer and System Sciences, 61, 108-143 (2000) · Zbl 0956.68055 · doi:10.1006/jcss.1999.1693 |
| [3] | Deng, X.; Dong, J.; Wang, S.; Luo, B.; Feng, H.; Zhang, G., Reducer lubrication optimization with an optimization spiking neural P system, Information Sciences, 604, 28-44 (2022) · doi:10.1016/j.ins.2022.05.016 |
| [4] | Huang, L.; Sun, L.; Wang, N.; Jin, X., Multiobjective optimization of simulated moving bed by tissue P system, Chinese Journal of Chemical Engineering, 15, 5, 683-690 (2007) · doi:10.1016/S1004-9541(07)60146-3 |
| [5] | Bo, W.; Fang, ZB; Wei, LI; Cheng, ZF; Hua, ZX, Malicious URLs detection based on a novel optimization algorithm, IEICE Transactions on Information and Systems, 104, 4, 513-516 (2021) · doi:10.1587/transinf.2020EDL8147 |
| [6] | Dong, J.; Zhang, G.; Luo, B.; Yang, Q.; Guo, D.; Rong, H.; Zhu, M.; Zhou, K., A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems, Information Sciences, 596, 1-14 (2022) · Zbl 1540.90233 · doi:10.1016/j.ins.2022.03.007 |
| [7] | Dong, J.; Zhang, G.; Luo, B., Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems, Journal of Membrane Computing, 4, 209-221 (2022) · Zbl 1518.68106 · doi:10.1007/s41965-022-00105-6 |
| [8] | Zhang, G.; Rong, H.; Neri, F.; Pérez-jiménez, MJ, An optimization spiking neural P system for approximately solving combinatorial optimization problems, International Journal of Neural Systems (2014) · doi:10.1142/S0129065714400061 |
| [9] | Zhu, M.; Yang, Q.; Dong, J.; Zhang, G.; Gou, X.; Rong, H.; Paul, P.; Neri, F., An adaptive optimization spiking neural P system for binary problems, International Journal of Neural Systems (2021) · doi:10.1142/S0129065720500549 |
| [10] | Buiu, C., et al. http://membranecomputing.net/. Accessed 1 June 2023. |
| [11] | Florea, AG; Buiu, C., Development of a software simulator for P colonies. Applications in robotics, International Journal of Unconventional Computing, 12, 2-3, 189-205 (2016) |
| [12] | Luo, Y.; Guo, P.; Zhang, M., A framework of ant colony P system, IEEE Access, 7, 157655-157666 (2019) · doi:10.1109/ACCESS.2019.2949952 |
| [13] | Ramachandranpillai, R.; Arock, M., Spiking neural P ant optimisation: A novel approach for ant colony optimisation, Electronics Letters, 56, 1320-1322 (2020) · doi:10.1049/el.2020.2144 |
| [14] | Ionescu, M.; Pǎun, G.; Yokomori, T., Spiking neural P systems, Fundamenta Informaticae, 71, 2-3, 279-308 (2006) · Zbl 1110.68043 |
| [15] | Gheorghe, M.; Stamatopoulou, I.; Holcombe, M.; Kefalas, P.; Banâtre, JP; Fradet, P.; Giavitto, JL; Michel, O., Modelling dynamically organised colonies of bio-entities, Unconventional programming paradigms. UPP. Lecture Notes in Computer Science (2004), Springer · doi:10.1007/11527800_17 |
| [16] | Kefalas, P.; Stamatopoulou, I.; Eleftherakis, G.; Gheorghe, M.; Corne, DW; Frisco, P.; Păun, G.; Rozenberg, G.; Salomaa, A., Transforming state-based models to P systems models in practice, Membrane computing. WMC. Lecture Notes in Computer Science (2008), Springer · doi:10.1007/978-3-540-95885-7_19 |
| [17] | Cienciala, L.; Ciencialová, L.; Perdek, M.; Csuhaj-Varjú, E.; Gheorghe, M.; Rozenberg, G.; Salomaa, A.; Vaszil, Gy, 2D P colonies, Membrane computing. CMC 2012. Lecture Notes in Computer Science, 161-172 (2012), Berlin: Springer, Berlin · Zbl 1388.68045 · doi:10.1007/978-3-642-36751-9_12 |
| [18] | Kelemen, J., Kelemenová, A., & Păun, G. (2004). Preview of P colonies: A biochemically inspired computing model. In: Workshop and Tutorial Proceedings. In: Ninth International Conference on the Simulation and Synthesis of Living Systems (Alife IX). pp. 82-86. Boston, Massachusetts, USA (September 12-15 2004) |
| [19] | Valenta, D.; Langer, M.; Ciencialová, L.; Cienciala, L.; Freund, R.; Ishdorj, TO; Rozenberg, G.; Salomaa, A.; Zandron, C., On Numerical 2D P colonies with the blackboard and the gray wolf algorithm, Membrane computing. CMC 2020. Lecture Notes in Computer Science (2021), Springer · Zbl 1542.68055 · doi:10.1007/978-3-030-77102-7_10 |
| [20] | Valenta, D.; Langer, M., On Numerical 2D P colonies modelling the grey wolf optimization algorithm, Processes., 9, 2, 330 (2021) · doi:10.3390/pr9020330 |
| [21] | Hunter, J. D. (2007). Matplotlib: “A 2D Graphics Environment”. Computing in Science and Engineering, 9(3), 90-95. doi:10.1109/MCSE.2007.55 |
| [22] | Rossum, G., & Drake, F. L. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace. https://docs.python.org/3/library/random.html. Accessed 1 June 2023. |
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