Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions
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This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization 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. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima.
KeywordsMultimodal function optimization Ant colony optimization Particle swarm optimization Glowworm swarm optimization Multiple signal source localization
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- Brits, R., Engelbrecht, A. P., & van den Bergh, F. (2002). A niching particle swarm optimizer. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (pp. 692–696). Google Scholar
- Clerc (2007). Particle swarm optimization. London: ISTE Ltd. Google Scholar
- Fevrier, V., & Patricia, M. (2007). Parallel evolutionary computing using a cluster for mathematical function optimization. In Proceedings of the annual meeting of the North American fuzzy information processing society (pp. 598–603). Piscataway: IEEE Press. Google Scholar
- Kennedy, J. (2000). Stereotyping: improving particle swarm performance with cluster analysis. In Proceedings of the congress on evolutionary computation (pp. 1507–1512). Piscataway: IEEE Press. Google Scholar
- Krishnanand, K. N. (2007). Glowworm swarm optimization: a multimodal function optimization paradigm with applications to multiple signal source localization tasks. PhD thesis, Department of Aerospace Engineering, Indian Institute of Science. Google Scholar
- Krishnanand, K. N., Amruth, P., Guruprasad, M. H., Bidargaddi, S. V., & Ghose, D. (2006a). Rendezvous of glowworm-inspired robot swarms at multiple source locations: a sound source based real-robot implementation. In M. Dorigo et al. (Eds.), Lecture notes in computer science : Vol. 4150. Ant colony optimization and swarm intelligence (pp. 259–269). Berlin: Springer. Google Scholar
- Krishnanand, K. N., Amruth, P., Guruprasad, M. H., Bidargaddi, S. V., & Ghose, D. (2006b). Glowworm-inspired robot swarm for simultaneous taxis toward multiple radiation sources. In Proceedings of IEEE international conference on robotics and automation (pp. 958–963). Piscataway: IEEE Press. Google Scholar
- Singh, G., & Deb, K. (2006). Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In Proceedings of the genetic and evolutionary computation conference (pp. 1305–1312). New York: ACM Press. Google Scholar
- Tyler, J. (1994). Glow-worms. Sevenoaks: Tyler-Scagell. Google Scholar