A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design

Leandro dos Santos Coelho, Emerson Hochsteiner de Vasconcelos Segundo, Fabio Alessandro Guerra, Viviana Cocco Mariani

2014

Abstract

The classical particle swarm optimization (PSO) algorithm is inspired on biological behaviors such as the social behavior of bird flocking and fish schooling. In this context, many significant improvements related the updating formulas and new operators have been proposed to improve the performance of the PSO algorithm in the literature. On the other hand, recently, as an alternative to the classical PSO, a quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The contribution of this paper is linked with a modified QPSO based on beta probability distribution and mutation differential operator. The effectiveness of the proposed modified QPSO algorithm is demonstrated by solving three kinds of optimization problems including two benchmark functions and a circular antenna design problem.

References

  1. Aote, S. S., Raghuwanshi, M. M. and Malik, L. (2013). Brief review on particle swarm optimization: limitations & future directions. International Journal of Computer Science Engineering (IJCSE) 2(5): 196- 200.
  2. Clerc, M. and Kennedy, J. F. (2002). The particle swarm: explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1): 58-73.
  3. Coelho, L. S. and Mariani, V. C. (2008). Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Conversion and Management 49(11): 3080-3085.
  4. Das, S. and Suganthan, P. N. (2010). Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems, Technical Report, Case study #9: Circular Antenna Array Design Problem, December.
  5. Das, S. and Suganthan, P. N. (2011). Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1): 4- 31.
  6. Eberhart, R. C. and Kennedy, J. F. (1995). A new optimizer using particle swarm optimization. In Proceedings of the International Symposium on Micro Machine and Human Science, Japan, 39-45.
  7. Eslami, M., Sharref, H., Khajehzadeh, M. and Mohamed, A. (2012). A survey of the state of the art in particle swarm optimization. Research Journal of Applied Sciences, Engineering and Technology 4(9): 1181- 1197.
  8. Fang, W., Sun, J., Ding, Y., Wu, X., and Xu, W. (2010). A review of quantum-behaved particle swarm optimization. IETE Technical Review 27(4): 336-348.
  9. Griewank A.O. (1981). Generalized descent for global optimization. Journal of Optimization Theory and Applications 34: 11-39.
  10. Han, K. H. and Kim, J.H. (2002). Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6(6): 580-593.
  11. IEEE-CEC, New Orleans, USA (2011). Competition on testing evolutionary algorithms on real-world numerical optimization problems, Detailed results and software in Matlab, http://www3.ntu.edu.sg/home/ epnsugan/index_files/CEC11-RWP/CEC11-RWP.htm.
  12. Kamberaj, H. (2014). Q-Gaussian swarm quantum particle intelligence on predicting global minimum of potential energy function. Applied Mathematics and Computation 229: 94-106.
  13. Kennedy, J. F. and Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE Conference on Neural Networks, Perth, Australia, 1942-1948.
  14. Mariani, V. C., Duck, A. R. K., Guerra, F. A., Coelho, L. S., and Rao, R. V. (2012). A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers. Applied Thermal Engineering 42: 119-128.
  15. Rini, D. P., Shamsuddin, S. M., and Yuhaniz, S. S. (2011). Particle swarm optimization: technique, system and challenges. International Journal of Computer Applications 14(1): 19-27.
  16. Rosenbrock, H. H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal 3: 175-184.
  17. Sedighizadeh, D. and Masehian, E. (2009). An particle swarm optimization method, taxonomy and applications. International Journal of Computer Theory and Engineering 5: 486-502.
  18. Storn, R. and Price, K. (1997). Differential evolution ? a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4): 341-359.
  19. Sun, J., Feng, B., and Xu, W. B. (2004a). Particle swarm optimization with particles having quantum behavior. In Proceedings of Congress on Evolutionary Computation, Portland, Oregon, USA, 325-331.
  20. Sun, J., Lai, C. -H., and Wu, X. -J. (2011). Particle swarm optimisation: classical and quantum perspectives. Boca Raton, USA, CRC Press.
  21. Sun, J., Wu, X., Palade, V., Fang, W., Lai, C. -H., and Xu, W. (2012). Convergence analysis and improvements of quantum-behaved particle swarm optimization. Information Sciences 193: 81-103.
  22. Sun, J., Xu, W. B., and Feng, B. (2004b). A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 111- 116.
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Paper Citation


in Harvard Style

dos Santos Coelho L., Hochsteiner de Vasconcelos Segundo E., Alessandro Guerra F. and Cocco Mariani V. (2014). A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 192-197. DOI: 10.5220/0005070201920197


in Bibtex Style

@conference{ecta14,
author={Leandro dos Santos Coelho and Emerson Hochsteiner de Vasconcelos Segundo and Fabio Alessandro Guerra and Viviana Cocco Mariani},
title={A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={192-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005070201920197},
isbn={978-989-758-052-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design
SN - 978-989-758-052-9
AU - dos Santos Coelho L.
AU - Hochsteiner de Vasconcelos Segundo E.
AU - Alessandro Guerra F.
AU - Cocco Mariani V.
PY - 2014
SP - 192
EP - 197
DO - 10.5220/0005070201920197