Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 2 | 155--161
Tytuł artykułu

Fully Informed Swarm Optimization Algorithms: Basic Concepts, Variants and Experimental Evaluation

Warianty tytułu
Języki publikacji
Particle swarm optimization constitutes currently one of the most important nature-inspired metaheuristics, used successfully for both combinatorial and continuous problems. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members solving optimization problem in hand. This paper overviews some examples of such techniques, namely Fully Informed Particle Swarm Optimization (FIPSO), Firefly Algorithm (FA) and Glowworm Swarm Optimization (GSO). It underlines similarities and differences among them and studies their practical features. Performance of those algorithms is also evaluated over a set of benchmark instances. Finally, some concluding remarks regarding the choice of suitable problem-oriented optimization technique along with areas of possible improvements are given as well.(original abstract)
Opis fizyczny
  • Cracow University of Technology, Poland
  • Polish Academy of Sciences
  • Clerc M. and Kennedy J., "The particle swarm - explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002. doi: 10.1109/4235.985692. [Online]. Available:
  • Cura T., "A particle swarm optimization approach to clustering," Expert Systems with Applications, vol. 39, no. 1, pp. 1582-1588, 2012. doi: 10.1016/j.eswa.2011.07.123. [Online]. Available:
  • Fang W., Sun J., Ding Y., Wu X. X., and Xu W., "A Review of Quantum-behaved Particle Swarm Optimization," IETE Technical Review, vol. 27, pp. 336-348, 2010.
  • Havens T., Spain C., Salmon N., and Keller J., "Roach infestation optimization," in Proceedings of the IEEE Swarm Intelligence Symposium 2008, 2008. doi: 10.1109/SIS.2008.4668317 pp. 1-7. [Online]. Available:
  • Hongliang L., Howely E., and Duggan J., "Particle Swarm Optimisation with Gradually Increasing Directed Neighbourhoods," in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 2011. doi: 10.1145/2001576.2001582 pp. 29-36. [Online]. Available:
  • Karegowda A. and Prasad M., "A Survey of Applications of Glowworm Swarm Optimization Algorithm," in IJCA Proceedings of the International Conference on Computing and Information Technology 2013, 2013, pp. 39-42.
  • Kennedy J. and Eberhart R., "Particle Swarm Optimization," in Proceedings of IEEE International Conference on Neural Networks, vol. IV, 1995. doi: 10.1109/ICNN.1995.488968 pp. 1942-1948. [Online]. Available:
  • Krishnanand K. and Ghose D., "Glowworm Swarm Optimization for Simultaneous Capture of Multiple Local Optima of Multimodal Functions," Swarm Intelligence, vol. 3, no. 2, pp. 87-124, 2008. doi: 10.1007/s11721-008-0021-5. [Online]. Available:
  • Krishnanand K. and Ghose D., "Theoretical Foundations for Rendezvous of Glowworm-inspired Agent Swarms at Multiple Locations," Robotics and Autonomous Systems, vol. 56, no. 7, pp. 549-569, 2008. doi: 10.1016/j.robot.2007.11.003. [Online]. Available:
  • Liang J., Qu B., Suganthan P., and Hernandez-Diaz A., "Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization," 2013, technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore.
  • Łukasik S. and Kowalski P., "Reviewing Fully Informed Swarm Optimization Algorithms," 2014, in preparation.
  • Łukasik S. and Żak S., "Firefly Algorithm for Continuous Constrained Optimization," Lecture Notes in Artificial Intelligence, vol. 5796, pp. 97-106, 2009. doi: 10.1007/978-3-642-04441-0_8. [Online]. Available:
  • Mendes R., Kennedy J., and Neves J., "The Fully Informed Particle Swarm: Simpler, Maybe Better," IEEE Transactions on Evolutionary Computation, vol. 8, no. 2, pp. 204-210, 2004. doi: 10.1109/TEVC.2004.826074. [Online]. Available:
  • Morsly Y., Aouf N., Djouadi M., and Richardson M., "Particle Swarm Optimization Inspired Probability Algorithm for Optimal Camera Network Placement," IEEE Sensors Journal, vol. 12, no. 5, pp. 1402-1412, 2012. doi: 10.1109/JSEN.2011.2170833. [Online]. Available:
  • Oca M. A. M. de and Stutzle T., "Convergence Behavior of the Fully Informed Particle Swarm Optimization Algorithm," in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, 2008. doi: 10.1145/1389095.1389106 pp. 71-78. [Online]. Available:
  • Oca M. A. M. de, Stutzle T., Birattari M., and Dorigo M., "Frankenstein's PSO: A composite particle swarm optimization algorithm," IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 1120-1132, 2009. doi: 10.1109/TEVC.2009.2021465. [Online]. Available:
  • Oramus P., "Improvements to Glowworm Swarm Optimization algorithm," Computer Science, vol. 11, pp. 7-20, 2010. doi: 10.7494/csci.2010.11.0.7. [Online]. Available:
  • Pham D., Ghanbarzadeh A., Koç E., Otri S., Rahim S., and Zaidi M., "The Bees Algorithm - A Novel Tool for Complex Optimisation Problems," in Proceedings of IPROMS 2006 Conference, 2006, pp. 454-461.
  • Röhler A. and Chen S., "Multi-swarm hybrid for multi-modal optimization," in Proceedings of the IEEE Congress on Evolutionary Computation, 2012. doi: 10.1109/CEC.2012.6256566 pp. 1759-1766. [Online]. Available:
  • Setayesh M., Zhang M., and Johnston M., "Effects of Static and Dynamic Topologies in Particle Swarm Optimisation for Edge Detection in Noisy Images," in Proceedings of the 2012 IEEE Congress on Evolutionary Computation, 2012. doi: 10.1109/CEC.2012.6256104 pp. 1-8. [Online]. Available:
  • Suganthan P., "Particle swarm optimiser with neighbourhood operator," in Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 3, vol. 3, 1999. doi: 10.1109/CEC.1999.785514 pp. 1958-1962. [Online]. Available:
  • Tehzeeb-Ul-Hassan H., Zafar R., Mohsin S., and Lateef O., "Reduction in power transmission loss using fully informed particle swarm optimization," International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 364 - 368, 2012. doi: 10.1016/j.ijepes.2012.05.028. [Online]. Available:
  • Yang X. and Gandomi A., "Bat algorithm: a novel approach for global engineering optimization," Engineering Computations, vol. 29, no. 5, pp. 464-483, 2012. doi: 10.1108/02644401211235834. [Online]. Available:
  • Yang X. and He X., "Firefly algorithm: recent advances and applications," International Journal of Swarm Intelligence, vol. 1, no. 1, pp. 36 - 50, 2013. doi: 10.1504/IJSI.2013.055801. [Online]. Available:
  • Yang X., "Firefly Algorithm, Lévy Flights and Global Optimization," in Research and Development in Intelligent Systems XXVI, M. Bramer, R. Ellis, and M. Petridis, Eds. Springer London, 2010, pp. 209-218. [Online]. Available:
  • Yang X., Nature-Inspired Metaheuristic Algorithms. Frome: Luniver Press, 2008.
  • Yuan-bin M., Yan-zhui M., and Qiao-yan Z., "Optimal Choice of Parameters for Firefly Algorithm," in Proceedings of the Fourth International Conference on Digital Manufacturing and Automation (ICDMA), 2013. doi: 10.1109/ICDMA.2013.210 pp. 887-892. [Online]. Available:
  • Zhang L., Yu H., and Hu S., "Optimal choice of parameters for particle swarm optimization," Journal of Zhejiang University Science A, vol. 6, no. 6, pp. 528-534, 2005. doi: 10.1007/BF02841760. [Online]. Available:
  • Zhou Y., Zhou G., and Zhang J., "A Hybrid Glowworm Swarm Optimization Algorithm for Constrained Engineering Design Problems," Applied Mathematics and Information Sciences, vol. 7, no. 1, pp. 379-388, 2013
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.