Email this article Development and Research of Distributed Control Algorithms for Swarm Intelligence Systems
- Authors: Ershov N.M.1
-
Affiliations:
- Lomonosov Moscow State University
- Issue: Vol 9, No 2 (2022)
- Pages: 21-34
- Section: Articles
- URL: https://bakhtiniada.ru/2313-223X/article/view/147132
- DOI: https://doi.org/10.33693/2313-223X-2022-9-2-21-34
- ID: 147132
Cite item
Open Access
Access granted
Subscription Access
Abstract
The subject of this paper is the development and research of distributed algorithms for organizing collective behavior in swarm robotic systems in order to solve various applied problems with these systems. Using the example of solving the problem of collective cleaning of a given area, several swarm algorithms based on classical swarm models are constructed and studied: random walk model, Reynolds model, bacterial search algorithm, stochastic gradient method. The results of numerical experiments comparing the efficiency of the proposed methods are presented
Full Text
##article.viewOnOriginalSite##About the authors
Nikolay M. Ershov
Lomonosov Moscow State University
Email: ershov@gse.cs.msu.ru
Cand. Sci. (Phys.-Math.); senior research at the Faculty of Computational Mathematics and Cybernetics Moscow, Russian Federation
References
- Beni G., Wang J. Swarm intelligence in cellular robotic systems, proceed. In: NATO advanced workshop on robots and biological systems. Tuscany, Italy, 1989. Pp. 703-712.
- Karpenko A.P. Modern algorithms for search optimization. Мoscow: Bauman MSTU, 2014.
- Dorigo M., Gambardella L.M. Ant Colony System: A cooperative learning approach to the traveling salesman problem // IEEE Transactions on Evolutionary Computation. 1997. No. 1 (1). Pp. 53-66.
- Sahin E. Swarm robotics: From sources of inspiration to domains of application. In: Swarm robotics. E. Sahin, W.M. Spears (eds.). 2005. LNCS 3342. Pp. 10-20.
- Ershov N.M. Introduction to distributed simulation in the NetLogo environment. Мoscow: DMK Press, 2018.
- Wilensky U., Rand W. An introduction to agent-based modeling; Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, Massachusetts: MIT Press, 2015.
- Nelson E. Dynamical theories of Brownian motion, mathematical notes. Princeton University Press, 1967.
- Xin-She Yang. Random walks and optimization, nature-inspired optimization algorithms. 2014. Pp. 45-65.
- Reynolds C.W. Flocks, herds and schools: A distributed behavioral model // Computer Graphics. 2021. No. 4. Pp. 25-34.
- Bayindir L. A Review of swarm robotics tasks // Neurocomputing. 2016. Vol. 172. Pp. 292-321.
- Passino K. Biomimicry of bacterial foraging for distributed optimization and control // IEEE Control Systems Magazine. 2002. No. 22. Pp. 52-67.
- Newton D., Pasupathy R., Yousefian F. Recent trends in stochastic gradient descent for machine learning and Big Data // Winter Simulation Conference. 2018. Pp. 366-380.
- Berdahl A., Torney C.J., Ioannou C.C. et al. Emergent sensing of complex environments by mobile animal groups // Science. 2013. No. 339 (6119). Pp. 574-576.
- Voevodin V.V., Voevodin Vl.V. Parallel computing. St. Petersburg: BHV-Petersburg, 2002.
Supplementary files
