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Glowworm swarm optimization

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The Glowworm Swarm Optimization (GSO) is a swarm intelligence optimization algorithm developed based on the behaviour of glowworms (also known as fireflies or lightning bugs). The behaviour pattern of glowworms which is used for this algorithm is the apparent capability of the glowworms to change the intensity of the luciferin emission and thus appear to glow at different intensities. The GSO algorithm makes the agents glow at intensities approximately proportional to the function value being optimized. It is assumed that glowworms of brighter intensities attract glowworms that have lower intensity. The second significant part of the algorithm incorporates a dynamic decision range by which the effect of distant glowworms are discounted when a glowworm has sufficient number of neighbours or the range goes beyond the range of perception of the glowworms. This algorithm allows swarms of glowworms to split into sub-groups and converge to high function value points. This property of the algorithm allows it to be used to identify multiple peaks of a multi-modal function.

The GSO algorithm was developed and introduced by K.N. Krishnanand and D. Ghose in 2005 at the Guidance, Control, and Decision Systems Laboratory in the Department of Aerospace Engineering at the Indian Institute of Science, Bangalore, India. Subsequently, it has been used in various applications and several papers have appeared in the literature using the GSO algorithm.

Recently (2008) an algorithm called the firefly algorithm has been proposed which largely follows the same principle as of the GSO algorithm except some variations in the way that the effect of neighboring glowworms are considered.

References

  • K.N. Krishnanand and D. Ghose: Glowworm swarm optimization: A new method for optimizing multimodal functions (tutorial paper), International Journal of Computational Intelligence Studies , Vol. 1, No. 1, pp. 93 - 119, 2009.
  • K.N. Krishnanand and D. Ghose. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, Vol. 3, No. 2, pp. 87-124, June 2009.
  • K.N. Krishnanand and D. Ghose. (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7): 549-569.
  • K.N. Krishnanand, P. Amruth, M.H. Guruprasad, S.V. Bidargaddi, and D. Ghose. (2006). Rendezvous of glowworm-inspired robot swarms at multiple source locations: A sound source based real-robot implementation. Ant Colony Optimization and Swarm Intelligence (Eds. M. Dorigo et al.), Lecture Notes in Computer Science, Springer Verlag, Berlin, Germany, LNCS 4317: 259-269.
  • K.N. Krishnanand and D. Ghose. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multi-agent and Grid Systems, Special Issue on Recent Progress in Distributed Intelligence, 2(3): 209-222.
  • K.N. Krishnanand and D. Ghose. (2006). Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains. American Control Conference, Minneapolis, Minnesota, USA, pp. 3588-3593.
  • K.N. Krishnanand, P. Amruth, M.H. Guruprasad, Sharschchandra V. Bidargaddi, and D. Ghose. (2006). Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources. IEEE International Conference on Robotics and Automation (ICRA 06), Orlando, Florida, USA, pp. 958-963.
  • K.N. Krishnanand and D. Ghose. (2006). Glowworm-inspired swarms with adaptive local-decision domains for multimodal function optimization. IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA.
  • K.N. Krishnanand and D. Ghose. (2005). Multimodal function optimization using a glowworm metaphor with applications to collective robotics. Second Indian International Conference on Artificial Intelligence (IICAI 05), Pune, India, pp. 328-346. (Best paper award).
  • K.N. Krishnanand and D. Ghose.(2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84-91.
  • K.N. Krishnanand and D. Ghose. (2007). Chasing multiple mobile signal sources: A glowworm swarm optimization approach. Third Indian International Conference on Artificial Intelligence (IICAI 07), Pune, India.
  • K.N. Krishnanand and D. Ghose. (2007). Glowworm swarm optimization algorithm for hazard sensing in ubiquitous environments. International Conference on Ubiquitous Information Technologies and Applications Dubai, UAE, pp. 1499-1501.
  • P. Amruth, K.N. Krishnanand, and D. Ghose. (2007). Glowworms-inspired multirobot system for multiple source localization tasks. Workshop on Multi-robot Systems for Societal Applications, International Joint Conference on Artificial Intelligence (IJCAI 07), Hyderabad, India, pp. 32-37.
  • K.N. Krishnanand and D. Ghose. A glowworm swarm optimization based multi-robot system for signal source localization. Design and Control of Intelligent Robotic Systems, Springer-Verlag, (Eds. D. Liu, L. Wang, and K.C. Tan), Studies in Computational Intelligence, Vol. 177, Springer-Verlag, Berlin, Germany, pp. 49-68, 2009.
  • K.N. Krishnanand and D. Ghose. (to appear in 2008). Glowworm swarm optimization algorithm for hazard sensing in ubiquitous environments using heterogeneous agent swarms. Soft Computing Applications in Industry, Springer-Verlag.
  • K.N. Krishnanand and D. Ghose: Glowworm Swarm Optimization for Searching Higher Dimensional Spaces, Swarm Intelligence for Knowledge-Based Systems (Eds. L.C. Jain, S. Dehuri, and C.P. Lim), Springer Verlag, Berlin, Germany (Accepted for Publication)
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