Glowworm Swarm Optimization for Searching Higher Dimensional Spaces
This chapter will deal with the problem of searching higher dimensional spaces using glowworm swarm optimization (GSO), a novel swarm intelligence algorithm, which was recently proposed for simultaneous capture of multiple optima of multimodal functions. Tests are performed on a set of three benchmark functions and the average peak-capture fraction is used as an index to analyze GSO’s performance as a function of dimension number. Results reported from tests conducted up to a maximum of eight dimensions show the efficacy of GSO in capturing multiple peaks in high dimensions. With an ability to search for local peaks of a function (which is the measure of fitness) in high dimensions, GSO can be applied to identification of multiple data clusters, satisfying some measure of fitness defined on the data, in high dimensional databases.
KeywordsParticle Swarm Optimization Multimodal Function Global Good Position Multiple Optimum Objective Function Space
Unable to display preview. Download preview PDF.
- Brits, R., Engelbrecht, A.P., van den Bergh, F.: A niching particle swarm optimizer. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 692–696 (2002)Google Scholar
- Clerc. Particle Swarm Optimization. ISTE Ltd., London (2007)Google Scholar
- Goldberg, D., Richardson, J.: Genetic algorithms with sharing for multi-modal function optimization. In: Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 44–49 (1987)Google Scholar
- Kennedy, J.: Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of the Congress on Evolutionary Computation, pp. 1507–1512 (2000)Google Scholar
- Krishnanand, K.N.: 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 (2007)Google Scholar
- Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Computational Intelligence Studies 1(1), 93–119 (2009)Google Scholar
- Li, X.: Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004)Google Scholar
- Singh, G., Deb, K.: Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1305–1312 (2006)Google Scholar
- Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem defintions and evaluation criteria for the cec 2005 special session on real-parameter optimization. In: Technical Report, Nanyang Technological University, Singapore and KanGAL Report No. 2005005, IIT Kanpur, India (2005)Google Scholar