Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems
- 281 Downloads
The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.
KeywordsShuffled frog-leaping algorithm Optimization Opposition-based learning Adaptive nonlinear inertia weight Perturbation operator strategy Gaussian mutation
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61403331, 61573306) and Natural Science Foundation of Hebei Province, China (Grant No. F2010001318).
- Alireza, R. V., Mostafa, D., Hamed, R., & Ehsan, S. (2008). A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 41, 1227–1239.Google Scholar
- Auger, A., & Hansen, N. (2005). Performance evaluation of an advanced local search evolutionary algorithm. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC’2005), pp. 1777–1784.Google Scholar
- Huynh, T.-H. (2008). A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. In Proceedings of international conference on information technology (ICIT 2008), Singapore, pp. 21–24.Google Scholar
- Luo, X., Yang, Y., & Li, X. (2009). Modified shuffled frog-leaping algorithm to solve traveling salesman problem. Journal of Communications, 30(7), 130–135.Google Scholar
- Purwoharjono, A., Muhammad, P., Ontoseno, S., et al. (2013). Optimal placement of TCSC using linear decreasing inertia weight gravitational search algorithm. Journal of Theoretical and Applied Information Technology, 47(2), 460–470.Google Scholar
- Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica D, 20(3), 710–720.Google Scholar
- Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315.Google Scholar
- Roy, P., Roy, P., & Chakrabarti, A. (2013). Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect. Applied Soft Computing, 13(11), 4244–4252.Google Scholar
- Stanarevic, N., Tuba, M., & Bacanin, N. (2011). Modified artificial bee colony algorithm for constrained problems optimization. International Journal of Mathematical Models and Methods in Applied Sciences, 5(3), 644–651.Google Scholar
- Xu, Q., Wang, L., He, B., & Wang, N. (2011). Modified opposition-based differential evolution for function optimization. Journal of Computational Information Systems, 7(5), 1582–1591.Google Scholar
- Yu, K., Wang, X., & Wang, Z. (2014). An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0918-3.
- Zhang, S., & Wong, T. N. (2014). Integrated process planning and scheduling: An enhanced ant colony optimization heuristic with parameter tuning. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-1023-3.