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A new quantum behaved particle swarm optimization

Published:12 July 2008Publication History

ABSTRACT

This paper presents a variant of Quantum behaved Particle Swarm Optimization (QPSO) named Q-QPSO for solving global optimization problems. The Q-QPSO algorithm is based on the characteristics of QPSO, and uses interpolation based recombination operator for generating a new solution vector in the search space. The performance of Q-QPSO is compared with Basic Particle Swarm Optimization (BPSO), QPSO and two other variants of QPSO taken from literature on six standard unconstrained, scalable benchmark problems. The experimental results show that the proposed algorithm outperforms the other algorithms quite significantly.

References

  1. Kennedy, J. and Eberhart, R. Particle Swarm Optimization. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, IV: 1942--1948, 1995.Google ScholarGoogle Scholar
  2. Liu J, Sun J, Xu W, Quantum--Behaved Particle Swarm Optimization with Adaptive Mutation Operator. ICNC 2006, Part I, Springer--Verlag: 959 -- 967, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Liu J, Xu W, Sun J. Quantum-Behaved Particle Swarm Optimization with Mutation Operator. In Proc. of the 17th IEEE Int. Conf. on Tools with Artificial Intelligence, Hong Kong (China), 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Millie Pant, Radha Thangaraj and Ajith Abraham, A New PSO Algorithm with Crossover Operator for Global Optimization Problems, Second International Symposium on Hybrid Artificial Intelligent Systems (HAIS'07), Advances in Softcomputing Series, Springer Verlag, Germany, E. Corchado et al. (Eds.): Innovations in Hybrid Intelligent Systems, Vol. 44, pp. 215--222, 2007.Google ScholarGoogle Scholar
  5. Millie Pant, Radha Thangaraj and Ajith Abraham, A New Particle Swarm Optimization Algorithm Incorporating Reproduction Operator for Solving Global Optimization Problems, 7th International Conference on Hybrid Intelligent Systems, Kaiserslautern, Germany, IEEE Computer Society press, USA, ISBN 07695-2662-4, pp. 144--149, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Pang XF, Quantum mechanics in nonlinear systems. River Edge (NJ, USA): World Scientific Publishing Company, 2005.Google ScholarGoogle Scholar
  7. Bin Feng, Wenbo Xu, Adaptive Particle Swarm Optimization Based on Quantum Oscillator Model. In Proc. of the 2004 IEEE Conf. on Cybernetics and Intelligent Systems, Singapore: 291 -- 294, 2004.Google ScholarGoogle Scholar
  8. Sun J, Feng B, Xu W, Particle Swarm Optimization with particles having Quantum Behavior. In Proc. of Congress on Evolutionary Computation, Portland (OR, USA), 325 -- 331, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  9. Sun J, Xu W, Feng B, A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization. In Proc. of the 2004 IEEE Conf. on Cybernetics and Intelligent Systems, Singapore: 291 -- 294, 2004.Google ScholarGoogle Scholar

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  1. A new quantum behaved particle swarm optimization

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    • Published in

      cover image ACM Conferences
      GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
      July 2008
      1814 pages
      ISBN:9781605581309
      DOI:10.1145/1389095
      • Conference Chair:
      • Conor Ryan,
      • Editor:
      • Maarten Keijzer

      Copyright © 2008 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 July 2008

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