Optimization Using Particle Swarms with Near Neighbor Interactions

  • Kalyan Veeramachaneni
  • Thanmaya Peram
  • Chilukuri Mohan
  • Lisa Ann Osadciw
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)


This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of the relative fitness and the distance of other particles to determine the direction in which each component of the particle position needs to be changed. The resulting algorithm, known as Fitness-Distance-Ratio based PSO (FDR-PSO), is shown to perform significantly better than the original PSO algorithm and several of its variants, on many different benchmark optimization problems. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO and several of its variants.


Particle Swarm Optimization Particle Swarm Particle Swarm Optimization Algorithm Benchmark Problem Premature Convergence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Kennedy, J. and Eberhart, R., “Particle Swarm Optimization”, IEEE International Conference on Neural Networks, 1995, Perth, Australia.Google Scholar
  2. 2.
    Eberhart, R. and Kennedy, J., “A New Optimizer Using Particles Swarm Theory”, Sixth International Symposium on Micro Machine and Human Science, 1995, Nayoga, Japan.Google Scholar
  3. 3.
    Eberhart, R. and Shi, Y., “Comparison between Genetic Algorithms and Particle Swarm Optimization”, The 7th Annual Conference on Evolutionary Programming, 1998, San Diego, USA.Google Scholar
  4. 4.
    Shi, Y. H., Eberhart, R. C., “A Modified Particle Swarm Optimizer”, IEEE International Conference on Evolutionary Computation, 1998, Anchorage, Alaska.Google Scholar
  5. 5.
    Kennedy J., “Small Worlds and MegaMinds: Effects of Neighbourhood Topology on Particle Swarm Performance”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1931–1938. IEEE Press.Google Scholar
  6. 6.
    Lovbjerg, M., Rasmussen, T. K., Krink, T., “ Hybrid Particle Swarm Optimiser with Breeding and Subpopulations”, Proceedings of Third Genetic Evolutionary Computation, (GECCO 2001).Google Scholar
  7. 7.
    Carlisle, A. and Dozier, G.. “Adapting Particle Swarm Optimization to Dynamic Environments”, Proceedings of International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, pp. 429–434, 2000.Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R. C., and Shi, Y. H.,_Swarm Intelligence, Morgan Kaufmann Publishers, 2001.Google Scholar
  9. 9.
    GEATbx: Genetic and Evolutionary Algorithm Toolbox for MATLAB, Hartmut Pohlheim,
  10. 10.
    E. Ozcan and C. K. Mohan, “Particle Swarm Optimzation: Surfing the Waves”, Proceedings of Congress on Evolutionary Computation (CEC’99), Washington D. C., July 1999, pp 1939–1944.Google Scholar
  11. 11.
    Particle Swarm Optimization Code, Yuhui Shi, Scholar
  12. 12.
    van den Bergh, F., Engelbrecht, A. P., “Cooperative Learning in Neural Networks using Particle Swarm Optimization”, South African Computer Journal, pp. 84–90, Nov. 2000.Google Scholar
  13. 13.
    van den Bergh, F., Engelbrecht, A. P., “Effects of Swarm Size on Cooperative Particle Swarm Optimisers”, Genetic and Evolutionary Computation Conference, San Francisco, USA, 2001.Google Scholar
  14. 14.
    Lovbjerg, M., Krink, T., “Extending Particle Swarm Optimisers with Self-Organized Criticality”, Proceedings of Fourth Congress on Evolutionary Computation, 2002, vol. 2, pp. 1588–1593.Google Scholar
  15. 15.
    Xiao-Feng Xie, Wen-Jun Zhang, Zhi-Lian Yang, “Hybrid Particle Swarm Optimizer with Mass Extinction”, International Conf. on Communication, Circuits and Systems (ICCCAS), Chengdu, China, 2002.Google Scholar
  16. 16.
    Xiao-Feng Xie, Wen-Jun Zhang, Zhi-Lian Yang, “A Dissipative Particle Swarm Optimization”, IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, USA, 2002.Google Scholar
  17. 17.
    Jacques Riget, Jakob S. Vesterstorm, “A Diversity-Guided Particle Swarm Optimizer-The ARPSO”, EVALife Technical Report no. 2002-02.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kalyan Veeramachaneni
    • 1
  • Thanmaya Peram
    • 1
  • Chilukuri Mohan
    • 1
  • Lisa Ann Osadciw
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuse

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