Interval Search with Quadratic Interpolation and Stable Deviation Quantum-Behaved Particle Swarm Optimization (IQS-QPSO)

Authors

  • P Amini
  • A Bagheri
  • S Moshfegh

DOI:

https://doi.org/10.21152/1750-9548.13.2.113

Abstract

In this article, in order to enhance the rate of convergence and scattering of particles at the same time, simple techniques are introduced. These techniques include: (1) Using the interval search to select a new particle candidate, (2) Replacement of three candidate particles instead to worst the particles in the population, (3) Using the best result of learning coefficients, (4) using a simple method to control the convergence of the algorithm in a high number of repetitions.

In this article, the performance of Quantum-Behaved Particle Swarm Optimization(QPSO) algorithm has been upgraded with using the interval search method. The proposed method of interval search of quantum-behaved particle swarm optimization algorithm has achieved better results than in the past with the use of quadratic interpolation recombination operator and stable deviation and interval search.

Moreover, the results of the proposed algorithm of Interval Search with Quadratic Interpolation and Stable Deviation Quantum-Behaved Particle Swarm Optimization  (IQS-QPSO) is compared with the other former algorithms such as Quantum-Behaved Particle Swarm Optimization (QPSO), Quadratic Interpolation Quantum-Behaved Particle Swarm Optimization (Q-QPSO) and Stable Deviation Quantum-Behaved Particle Swarm Optimization (SD-QPSO). Then the performance improvement is reported. In order to compare the results of each algorithm, five famous functions are used and consequently the results are reported separately for each function

References

Nariman-Zadeh, N., Atashkari, K., Jamali, A., Pilechi, A. and Yao, X., 2005. Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms. Engineering Optimization, 37(5), pp.437-462. https://doi.org/10.1080/03052150500035591

Parsopoulos, K.E. ed., 2010. Particle swarm optimization and intelligence: advances and applications: advances and applications. IGI global.

Mahmoodabadi, M.J., Mottaghi, Z.S. and Bagheri, A., 2014. HEPSO: high exploration particle swarm optimization. Information Sciences, 273, pp.101-111. https://doi.org/10.1016/j.ins.2014.02.150

Kennedy, J., & Eberhart, R. C.,1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.

Kwok, N.M., Liu, D.K. and Dissanayake, G., 2006. Evolutionary computing based mobile robot localization. Engineering Applications of Artificial Intelligence, 19(8), pp.857-868. https://doi.org/10.1016/j.engappai.2006.01.020

Mouser, C.R. and Dunn, S.A., 2005. Comparing genetic algorithms and particle swarm optimisation for an inverse problem exercise. ANZIAM Journal, 46, pp.89-101. https://doi.org/10.21914/anziamj.v46i0.949

Engelbrecht, A.P., 2007. Computational intelligence: an introduction. John Wiley & Sons.

Dorigo, M. and Thomas, S., 2004. Ant Colony Optimization. Cambridge, vol. 9, Dec. 2002.

Holden, N. and Freitas, A.A., 2005, June. A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. In Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE (pp. 100-107). IEEE. https://doi.org/10.1109/sis.2005.1501608

Hendtlass, T., 2001, June. A combined swarm differential evolution algorithm for optimization problems. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 11-18). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-45517-5_2

Zhang, W.J. and Xie, X.F., 2003, October. DEPSO: hybrid particle swarm with differential evolution operator. In Systems, Man and Cybernetics, 2003. IEEE International Conference on (Vol. 4, pp. 3816-3821). IEEE. https://doi.org/10.1109/icsmc.2003.1244483

Poli, R., Langdon, W.B. and Holland, O., 2005, March. Extending particle swarm optimisation via genetic programming. In European Conference on Genetic Programming (pp. 291-300). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-31989-4_26

Poli, R., Di Chio, C. and Langdon, W.B., 2005, June. Exploring extended particle swarms: a genetic programming approach. In Proceedings of the 7th annual conference on Genetic and evolutionary computation (pp. 169-176). ACM. https://doi.org/10.1145/1068009.1068036

Parsopoulos, K.E. and Vrahatis, M.N., 2004. On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), pp.211-224. https://doi.org/10.1109/tevc.2004.826076

Parsopoulos, K.E. and Vrahatis, M.N., 2001. Particle swarm optimizer in noisy and continuously changing environment. methods, 5(6), pp289-294.

Dawkins, R., 1976. The selfish gene New York: Oxford Univ.

van den Bergh, F. and Engelbrecht, A.P., 2002, October. A new locally convergent particle swarm optimiser. In Systems, Man and Cybernetics, 2002 IEEE International Conference on (Vol. 3, pp. 6-pp). IEEE. https://doi.org/10.1109/icsmc.2002.1176018

Clearwater, S.H., Hogg, T. and Huberman, B.A., 1992. Cooperative problem solving. Computation: The micro and the macro view, 275, pp.33-70. https://doi.org/10.1142/9789812812438_0003

Brits, R., Engelbrecht, A.P. and Van den Bergh, F., 2002, November. A niching particle swarm optimizer. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (Vol. 2, pp. 692-696). Singapore: Orchid Country Club.

Sun, J., Feng, B. and Xu, W., 2004, June. Particle swarm optimization with particles having quantum behavior. In Evolutionary Computation, 2004. CEC2004. Congress on (Vol. 1, pp. 325-331). IEEE. https://doi.org/10.1109/cec.2004.1330875

Feng, B. and Xu, W., 2004, December. Adaptive particle swarm optimization based on quantum oscillator model. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on (Vol. 1, pp. 291-294). IEEE. https://doi.org/10.1109/iccis.2004.1460428

Moghaddam, J.J. and Bagheri, A., 2015. A novel stable deviation quantum-behaved particle swarm optimization to optimal piezoelectric actuator and sensor location for active vibration control. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 229(6), pp.485-494. https://doi.org/10.1177/0959651815573897

Pant, M., Thangaraj, R. and Abraham, A., 2008, July. A new quantum behaved particle swarm optimization. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 87-94). ACM. https://doi.org/10.1145/1389095.1389108

Clerc, M. and Kennedy, J., 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation, 6(1), pp.58-73. 25. 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), pp.341-359. https://doi.org/10.1109/4235.985692

Published

2019-06-30

How to Cite

Amini, P., Bagheri, A. and Moshfegh, S. (2019) “Interval Search with Quadratic Interpolation and Stable Deviation Quantum-Behaved Particle Swarm Optimization (IQS-QPSO)”, The International Journal of Multiphysics, 13(2), pp. 113-130. doi: 10.21152/1750-9548.13.2.113.

Issue

Section

Articles