Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
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An Improvement of Particle Swarm Optimization with A Neighborhood Search Algorithm

  • Published : 2007.06.30
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Abstract

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

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References

  1. Clerc, M. (1999), The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), Washington, D. C., 1951-1957.
  2. Heppner, F. and Grenander, U. (1990), A stochastic nonlinear model for coordinated bird flocks. In Krasner, S. Ed., The ubiquity of chaos, AAAS Publications, Washington, DC.
  3. Kennedy, J. and Eberhart, R. C. (1995), Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ., 1942-1948.
  4. Kennedy, J., Eberhart, R. C., and Shi, Y. (2001), Swarm intelligence, Morgan Kaufmann Publishers, San Mateo, CA.
  5. Li, X. (2004), Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function, Proceedings of the Genetic and Evolutionary Computation Conference 2004, Seattle, Washington, 117-128.
  6. Li, X. (2004), Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization, Proceedings of the Genetic and Evolutionary Computation Conference 2004, Seattle, Washington, 105-116.
  7. Noel, M. M. and Jannett, T. C. (2004), Simulation of a new hybrid particle swarm optimization algorithm, Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, Atlanta, Georgia, 150-153.
  8. Parsopoulos, K. E. and Vrahatis, M. N. (2001), Modification of the Particle Swarm Optimizer for locating All the Global Minima, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, Prague, Czech Republic, 324-327.
  9. Parsopoulos, K. E. and Vrahatis, M. N. (2002), Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method,. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Clifton, New Jersey, 216-221.
  10. Reynolds, C. W. (1987), Flocks, herbs, and schools: A distributed behavioral model, Computer Graphics, 21(4), 25-34. https://doi.org/10.1145/37402.37406
  11. Richards, M. and Ventura, D. (2003), Dynamic Society in Particle Swarm Optimization, Proceedings of the International Conference on Computational Intelligence and Natural Computing, Cary, North Carolina, 1557-1560.
  12. Shi, Y. and Eberhart, R. C. (1998), A modified particle swarm optimizer, Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 69-73.
  13. Toyoda, Y., Yano, F., and Shohdohji, T. (2004), Dynamic Assignment of Parameter Values in Particle Swarm Optimization, Proceedings of the fifth Asia- Pacific Industrial Engineering and Management Systems Conference, Gold Coast, Australia, 32.9.1-32.9.10 (in CD-ROM).
  14. Veeramachaneni, K., Peram, T., Mohan, C. K., and Osadciw, L. A. (2003), Optimization Using Particle Swarms with Near Neighbor Interactions, Proceedings of Genetic and Evolutionary Computation Conference, Chicago, Illinois, 110-121.
  15. Wilson, E. O. (1975), Sociobiology: The new synthesis, Belknap Press, Cambridge, Massachusetts.
  16. Yano, F. and Toyoda Y. (2004), Properties of Particle Swarm Optimization applied to Multi-Hump Camel- Back Functions, Proceedings of the 33rd International Conference on Computers and Industrial Engineering, Jeju, Korea, (in CD-ROM).