Particle Swarm Optimization for Continuous Function Optimization Problems
DOI:
https://doi.org/10.18100/ijamec.2017331879Keywords:
continuous function optimization, global minimum, heuristic techniques, particle swarm optimizationAbstract
In this paper, particle swarm optimization is proposed for finding the global minimum of continuous functions and experimented on benchmark test problems. Particle swarm optimization applied on 21 benchmark test functions, and its solutions are compared to those former proposed approaches: ant colony optimization, a heuristic random optimization, the discrete filled function algorithm, an adaptive random search, dynamic random search technique and random selection walk technique. The implementation of the PSO on several test problems are reported with satisfactory numerical results when compared to previously proposed heuristic techniques. PSO is proved to be successful approach to solve continuous optimization problems.Downloads
References
Eberhart, R., & Kennedy, J. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). (1995), IEEE.
Clerc, M., & Kennedy, J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation, 6(1), (2002). 58-73.
Y.Yongjian, L.Yumei, A new discrete filled function algorithm for discrete global optimization, Journal of Computational and Applied Mathematics 202 (2007) 280 – 291.
C. Hamzacebi, F. Kutay, Continuous functions minimization by dynamic random search technique, Applied Mathematical Modelling 31 (2007) 2189-2198.
M. D. Toksari, Ant colony optimization for finding the global minimum, Applied Mathematics and Computation 176 (2006) 308–316.
J. Li, R. R. Rhinehart, Heuristic random optimization, Computers chem. Engng (1998) 22 427-444.
C. Hamzacebi, F. Kutay, A heuristic approach for finding the global minimum: Adaptive random search technique, Applied Mathematics and Computation 173 (2006) 1323–1333.
Cura, Tunchan. "A random search approach to finding the global minimum." Int. J. Contemp. Math. Science 5.4 (2010): 179-190.
Pan, Quan-Ke, et al. "An improved fruit fly optimization algorithm for continuous function optimization problems." Knowledge-Based Systems 62 (2014): 69-83.
Wang, Jie-Sheng, and Jiang-Di Song. "Application and Performance Comparison of Biogeography-based Optimization Algorithm on Unconstrained Function Optimization Problem." International Journal of Applied Mathematics 47.1 (2017).
Nabil, Emad. "A modified flower pollination algorithm for global optimization." Expert Systems with Applications 57 (2016): 192-203.
Guo, Ying, et al. "Function Optimization via a Continuous Action-Set Reinforcement Learning Automata Model." Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Springer Berlin Heidelberg, (2016).
Wang, Chun-Feng, and Yong-Hong Zhang. "An improved artificial bee colony algorithm for solving optimization problems." IAENG International Journal of Computer Science 43.3 (2016): 336-343.
Y. Liang, and K. S. Leung, “Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization,” Applied Soft Computing, vol. 11, no. 2, (2011), pp. 2017–2034
Downloads
Published
Issue
Section
License
Copyright (c) 2017 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
