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Further improvement of the Glowworm Swarm Optimization algorithm by adding a conservation of agent move direction

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Języki publikacji
EN
Abstrakty
EN
Glowworm Swarm Optimization is an algorithm, which can localize multiple optima of the multi-modal function during a single run. Unfortunately, original algorithm manifests several disadvantages: an agent can change its position only in the presence of other agents and exclusively in direction of randomly chosen neighboring agent. This paper shows how GSO algorithm can be significantly improved by simple modifications: agents receive an alternative method of changing the position by random jumps and they prefer directions consistent with the direction of the previous movement.
Rocznik
Strony
15--21
Opis fizyczny
Bibliogr. 15 poz., wykr.
Twórcy
autor
  • Department for Information Technology, Faculty of Physics, Astronomy and Applied Computer Science Jagiellonian University in Krakow Reymonta 4, 30-059 Kraków, Poland
Bibliografia
  • [1] Brits, R., Engelbrecht, A. P., van den Bergh F.: A niching particle swarm optimizer. Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, 2002, pp. 692–696.
  • [2] Kennedy J., Eberhart R.: Particle Swarm Optimization, Proc. IEEE International Conference on Neural Networks, 1995, pp. IV: 1942-1948.
  • [3] Mahfoud, S.W. Niching methods for genetic algorithms. Doctoral dissertation, University of Illinois at Urbana-Champaign. Dissertation Abstracts International 1995, 51(9), 4987B (University Microfilms No. 9543663).
  • [4] Krishnanand K. N., Ghose D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, 2005, pp. 84–91.
  • [5] Krishnanand K. N., Ghose D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multi-agent and Grid Systems 2006, 2(3): 209–222.
  • [6] Krishnanand K. N., Ghose D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 2009, 3, 2: 87–124.
  • [7] Oramus P.: Improvements to glowworm swarm optimization algorithm, Computer Science 2010, 11: 7-20.
  • [8] http://www.r-project.org/ (accessed 2011-03-30).
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  • [10] http://cran.r-project.org/web/packages/RSQLite/index.html (accessed 2011-03-30).
  • [11] Reutskiy, S. Y., Chen, C. S.: Approximation of multivariate functions and evaluation of particular solutions using Chebyshev polynomial and trigonometric basis functions. International Journal for Numerical Methods in Engineering 2006, 67(13): 1811–1829.
  • [12] Törn, A., Zilinskas, A.: Global optimization. New York: Springer. 1989.
  • [13] Parsopoulos, K., Vrahatis, M. N.: On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on Evolutionary Computation 2004, 8(3): 211–224.
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  • [15] http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf (accessed 2011-03-30).
Typ dokumentu
Bibliografia
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