Glowworm Swarm Optimization

This website describes a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The development of the algorithm, its capability to solve the odor source localization problem, simulation results, videos, experimental results etc are listed here. 

The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. Each glowworm uses the luciferin to (indirectly) communicate the function-profile information at its current location to its neighbors. The glowworm depends on a variable local-decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves towards it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. It is interesting to note that the splitting behavior of the glowworm swarm respects the voronoi partition of the peak locations of the multimodal function profile when the slopes of the individual peak profiles are equal.