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==Multimodal optimization using swarm intelligence-based GSO algorithm==
[[Glowworm swarm optimization]] (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of multiple optima of multimodal functions.<ref>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,</ref><ref>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.</ref><ref>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.</ref><ref>K. N. Krishnanand and D. Ghose. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multi-agent and Grid Systems, 2(3): 209–222.</ref> The algorithm shares a few features with some better known algorithms, such as [[ant colony optimization]] and [[particle swarm optimization]], but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. Unlike most other evolutionary multimodal optimization algorithms, the property of splitting into sub-groups allows the algorithm to simultaneously converge to local optima of different values, thus making it suitable for solving multiple signal source seeking problems using robots
multiple emission sources, ACM Computing Surveys, Vol. 43,
Issue 3, April 2011, pp. 15:1 – 15:25</ref>
==See also==
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