Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Concise Chronological Reassess of Different Swarm Intelligence Methods with Multi Robotics Approach


Affiliations
1 Department of Information Science and Engineering, MVJ College of Engineering, India
     

   Subscribe/Renew Journal


Swarm insight is the discipline that arrangements with normal and fake frameworks made out of numerous people that facilitate utilizing decentralized control and self-association. Specifically, the order focuses on the collective behaviours that outcome from the nearby cooperation’s of the people with one another and with their environment. We can discover swarm in provinces of ants, school of fishes, herds of feathered creatures and so on. The different Swarm Intelligence models, for example, the Ant Colony Optimization where it depicts about the development of ants, their conduct, and how do it conquer the impediments, in fowls we see about the Particle swarm advancement it depends on the swarm knowledge and how the positions must be put in view of the standards. Next is the Bee state streamlining that arrangements with the conduct of the honey bees, their associations, likewise portrays about the Movement and how they function as developing aggregate knowledge of gatherings of basic self-governing operators. As a new research territory by which swarm knowledge is connected to multi-robot frameworks; swarm mechanical technology thinks about how to facilitate extensive gatherings of generally straightforward robots using neighbourhood rules. It centers on concentrate the plan of huge measure of generally basic robots, their physical bodies and their controlling practices. Since its presentation in 2000, a few fruitful experimentations had been acknowledged, and till now more tasks are under examinations. This paper tries to give a review of this space look into for the aim to orientate the readers, particularly the individuals who are recently coming to this research field.

Keywords

Pheromone, Stigmergy, Particle Swarm Optimization, Ant Colony Optimization, Bee Colony Optimization.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Hazem Ahmed and Janice Glasgow, “Swarm Intelligence Concepts, Models and Applications”, Technical Report, School of Computing, Queens University, pp. 1-51, 2012
  • S. Keerthi, K. Ashwini and M.V. Vijay Kumar, “Survey Paper on Swarm Intelligence”, International Journal of Computer Applications, Vol. 115, No. 5, pp. 1-5, 2015
  • C. Blum and D. Merkle, “Swarm Intelligence-Introduction and Applications”, 1st Edition, Springer, 2008.
  • M. Belal, J. Gaber, H. El-Sayed and A. Almojel, “Swarm Intelligence”, 1st Edition, Springer, 2006.
  • B.K. Panigrahi, Y. Shi and M.H. Lim, “Adaptation, Learning, and Optimization”, Springer, 2009.
  • E. Lumer and B. Faieta, “Diversity and Adaptation in Populations of Clustering Ants”, Proceedings of 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 501-508, 1994.
  • P. Kuntz, P. Layzell and D. Snyers, “A Colony of Ant-like Agents for Partitioning in VLSI Technology”, Proceedings of 4th European Conference on Artificial Life, pp. 417-424, 1997.
  • L. Rosenberg, “Human Swarms a Real-Time Method for Collective Intelligence”, Proceedings of International Conference on Artificial Life, pp. 658-659, 2015.
  • E. Bonabeau, M. Dorigo and G. Theraulaz, “Swarm Intelligence: from Natural to Artificial Systems”, Oxford University Press, 1999.
  • M. Dorigo, M. Birattari and T. Stutzle, “Ant Colony Optimization-Artificial Ants as a Computational Intelligence Technique”, IEEE Computational Intelligence Magazine, Vol. 1, pp. 28-39, 2006.
  • D. Marco, “The SWARM-BOT Project. Swarm Robotics”, Proceedings of IEEE Swarm Intelligence Symposium, pp. 129-135, 2005.
  • N. Mehdi et al., “Artificial Fish Swarm Algorithm: A Survey of the State of the-Art, Hybridization, Combinatorial and Indicative Applications”, Artificial Intelligence Review, Vol. 42, No. 4, pp. 965-997, 2012.
  • T. Stirling and D. Floreano, “Energy Efficient Swarm Deployment for Search in Unknown Environments”, Proceedings of 7th International Conference on Swarm Intelligence, pp. 562-563, 2010.
  • N, Bighnaraj et al., “Cooperative Swarm based Evolutionary Approach to find Optimal Cluster Centroids in Cluster Analysis”, International Journal of Computer Science Issues, Vol. 9, No. 3, pp. 425-434, 2012.
  • J. Aleksandar and A. Diego, “Swarm Intelligence and Its Applications in Swarm Robotics”, Proceedings of 6th International Conference on Computational Intelligence. Man Machine Systems and Cybernetics, pp. 14-17, 2007
  • Eric Bonabeau, Marco Dorigo and Guy Theraulaz, “Swarm Intelligence from Natural to Artificial Systems”, Oxford University Press, 1999
  • T. Stirling and D. Florean, “Energy Efficient Swarm Deployment for Search in Unknown Environments”, Proceedings of 7th International Conference on Swarm Intelligence, pp. 562-563, 2010.
  • F. Ducatelle et al., “Self-Organized Cooperation between Robotic Swarms”. Swarm Intelligence, Vol. 5, No. 2, pp. 73-96, 2011.
  • A.E. Turgut , H. Celikkanat, F. Gokce and E. Sahin “Self-Organized Flocking in Mobile Robot Swarms”, Swarm Intelligence, Vol. 2, No. 2, pp. 97-120, 2008.
  • B. Grob and M. Dorigo, “Evolution of Solitary and Group Transport Behaviors for Autonomous Robots Capable of Self-Assembling”, Adaptive Behavior, Vol. 16, No. 5, pp. 285-305, 2008.
  • S. Garnier, “Aggregation Behaviour as a Source of Collective Decision in a Group of Cockroach-like Robots”, Proceedings of International Conference on Advances in Artificial Life, pp. 169-178, 2005.
  • A. Gutierrez et al., “Collective Decision Making based on Social Odometry”, Neural Computing and Applications, Vol. 19, No. 6, pp. 807-823, 2010.
  • G. Pini, “Task Partitioning in Swarms of Robots an Adaptive Method for Strategy Selection”, Available at:http://iridia.ulb.ac.be/supp/IridiaSupp2011- 003/index.html, Accessed on 2011.
  • Krishna H. Hingrajiya, Ravindra Kumar Gupta and Gajendra Singh Chandel, “An Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem”, International Journal of Scientific and Research Publications, Vol. 2, No. 8, pp. 1-6, 2012.
  • Haitham Bou Ammar, Karl Tuyls and Michael Kaisers, “Evolutionary Dynamics of Ant Colony Optimization”, Proceedings of German Conference on Multiagent System Technologies, pp. 40-52, 2012.
  • Yassiah Bissiri, W. Scott Dunbar and Allan Hall, “Swarm based Truck-Shovel Dispatching System in Open Pit Mine Operations”, Master Thesis, Department of Mining and Mineral Process Engineering, University of British Columbia, 2001.
  • M. Beekman, G.A. Sword and S.J. Simpson, “Biological Foundations of Swarm Intelligence”, Swarm Intelligence, 2008.
  • Bijaya Ketan Panigrahi et al., “Handbook of Swarm Intelligence Concepts, Principles and Applications”, Springer, 2011.
  • M. Fleischer, “Foundations of Swarm Intelligence: From Principles to Practice”, Available at: https://arxiv.org/pdf/nlin/0502003.pdf.
  • T. Ying, “Research Advance in Swarm Robotics”, Defence Technology, Vol. 9, No. 1, pp. 18-39, 2013.
  • J.C.S. Amanda, “Swarm Robotics and Minimalism”, Connection Science, Vol. 19, No. 3, pp. 245-260, 2017.

Abstract Views: 375

PDF Views: 0




  • A Concise Chronological Reassess of Different Swarm Intelligence Methods with Multi Robotics Approach

Abstract Views: 375  |  PDF Views: 0

Authors

K. Priya
Department of Information Science and Engineering, MVJ College of Engineering, India

Abstract


Swarm insight is the discipline that arrangements with normal and fake frameworks made out of numerous people that facilitate utilizing decentralized control and self-association. Specifically, the order focuses on the collective behaviours that outcome from the nearby cooperation’s of the people with one another and with their environment. We can discover swarm in provinces of ants, school of fishes, herds of feathered creatures and so on. The different Swarm Intelligence models, for example, the Ant Colony Optimization where it depicts about the development of ants, their conduct, and how do it conquer the impediments, in fowls we see about the Particle swarm advancement it depends on the swarm knowledge and how the positions must be put in view of the standards. Next is the Bee state streamlining that arrangements with the conduct of the honey bees, their associations, likewise portrays about the Movement and how they function as developing aggregate knowledge of gatherings of basic self-governing operators. As a new research territory by which swarm knowledge is connected to multi-robot frameworks; swarm mechanical technology thinks about how to facilitate extensive gatherings of generally straightforward robots using neighbourhood rules. It centers on concentrate the plan of huge measure of generally basic robots, their physical bodies and their controlling practices. Since its presentation in 2000, a few fruitful experimentations had been acknowledged, and till now more tasks are under examinations. This paper tries to give a review of this space look into for the aim to orientate the readers, particularly the individuals who are recently coming to this research field.

Keywords


Pheromone, Stigmergy, Particle Swarm Optimization, Ant Colony Optimization, Bee Colony Optimization.

References