Abstract
The paper studies the problem of continuous function optimization. Proposed are twelve hybrids of known methods such as Particle Swarm Optimization (also in a two-subpopulation version), quasi-Newton method and Nelder-Mead method. Described modifications are introduced in order to improve performance and increase the accuracy of known methods. Algorithms are tested against eight benchmark functions and compared with classical versions of: Particle Swarm Optimization algorithm, Newton’s, quasi-Newton and Nelder-Mead methods. Presented results allow to indicate two methods that perform satisfactorily in most cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Global Optimization Benchmarks and AMPGO, http://infinity77.net/global_optimization/test_functions.html (accessed September 26, 2014)
Ali, M.M., Kaelo, P.: Improved particle swarm algorithms for global optimization. Applied Mathematics and Computation 196(2), 578–593 (2008)
Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88 (2000)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948 (November 1995)
MathWorld: Fisher’s Exact Test (1999-2015), http://mathworld.wolfram.com/FishersExactTest.html , (accessed December 27, 2014)
Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. The Computer Journal 7(4), 308–313 (1965)
Nocedal, J., Wright, S.: Numerical Optimization. Springer series in operations research and financial engineering, Springer (1999)
dos Santos Coelho, L., Mariani, V.: Particle Swarm Optimization with Quasi-Newton Local Search for Solving Economic Dispatch Problem. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 4, pp. 3109–3113 (October 2006)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3. IEEE (1999)
Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Thomas, N., Reed, M.: A hybrid algorithm for continuous optimisation. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 2584–2589 (May 2009)
Zhe-ping, Y., Chao, D., Jia-jia, Z., Dong-nan, C.: A novel two-subpopulation particle swarm optimization. In: 10th World Congress on Intelligent Control and Automation (WCICA), pp. 4113–4117 (July 2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bera, A., Sychel, D. (2015). Hybrids of Two-Subpopulation PSO Algorithm with Local Search Methods for Continuous Optimization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-19324-3_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
eBook Packages: Computer ScienceComputer Science (R0)