Journal of Intelligent Manufacturing

, Volume 29, Issue 5, pp 1133–1153 | Cite as

Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Article

Abstract

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.

Keywords

Shuffled frog-leaping algorithm Optimization Opposition-based learning Adaptive nonlinear inertia weight  Perturbation operator strategy Gaussian mutation 

Notes

Acknowledgments

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).

References

  1. Ahandani, M. A., & Alavi-Rad, H. (2015). Opposition-based learning in shuffled frog leaping: An application for parameter identification. Information Sciences, 291, 19–42.CrossRefGoogle Scholar
  2. Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23, 1001–1014.CrossRefGoogle Scholar
  3. 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
  4. Al-Tabtabai, H., & Alex, P. A. (1999). Using genetic algorithms to solve optimization problems in construction. Engineering Construction and Architectural Management, 6(2), 121–132.CrossRefGoogle Scholar
  5. 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
  6. Brajevic, I., & Tuba, M. (2013). An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24, 729–740.CrossRefGoogle Scholar
  7. Ding, J., Liu, J., Chowdhury, K. R., et al. (2014). A particle swarm optimization using local stochastic search and enhancing diversity for continuous optimization. Neurocomputing, 137, 261–267.CrossRefGoogle Scholar
  8. Elbeltagi, E., Hegazy, T., & Gridrson, D. (2007). A modified shuffled frog-leaping optimization algorithm: Applications to project management. Structure and Infrastructure Engineering, 3(1), 53–60.CrossRefGoogle Scholar
  9. Eslami, M., Shareef, H., Mohamed, A., et al. (2012). An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems. International Journal of Electrical Power and Energy Systems, 43(1), 1467–1478.CrossRefGoogle Scholar
  10. Eusuff, M., & Lansey, K. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 129(3), 10–25.CrossRefGoogle Scholar
  11. Guo, L., Wang, G.-G., Gandomi, A. H., et al. (2014). A new improved krill herd algorithm for global numerical optimization. Neurocomputing, 138, 392–402.CrossRefGoogle Scholar
  12. 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
  13. Jin, Y., Guan, Y., & Zheng, L. (2011). An image encryption algorithm based on chaos. Advances in Intelligent and Soft Computing, 106, 493–497.CrossRefGoogle Scholar
  14. Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697.CrossRefGoogle Scholar
  15. Lakshmi, K., & Rao, A. R. M. (2013). Hybrid shuffled frog leaping optimization algorithm for multi-objective optimal design of laminate composites. Computers & Structures, 125, 200–216.CrossRefGoogle Scholar
  16. Li, M., Lin, D., & Kou, J. (2012). A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Applied Soft Computing, 12(3), 975–987.CrossRefGoogle Scholar
  17. Li, X., Liu, L., Wang, N., & Pan, J.-S. (2011). A new robust watermarking scheme based on shuffled frog leaping algorithm. Intelligent Automation and Soft Computing, 17(2), 219–231.CrossRefGoogle Scholar
  18. Li, X., Luo, J., Chen, M.-R., et al. (2012). An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimization. Information Sciences, 192, 143–151.CrossRefGoogle Scholar
  19. Li, G., Niu, P., & Xiao, X. (2012). Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied Soft Computing, 12(1), 320–332.CrossRefGoogle Scholar
  20. 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
  21. Lu, X., Tang, K., Sendhoff, B., & Yao, X. (2014). A new self-adaptation scheme for differential evolution. Neurocomputing, 146, 2–16.CrossRefGoogle Scholar
  22. Niknam, T., Firouzi, B. B., & Mojarrad, H. D. (2011). A new evolutionary algorithm for non-linear economic dispatch. Expert Systems with Applications, 38(10), 13301–13309.CrossRefGoogle Scholar
  23. Oyeka, I. C. A., & Ebuh, G. U. (2012). Modified Wilcoxon signed-rank test. Open Journal of Statistics, 2, 172–176.CrossRefGoogle Scholar
  24. Pan, Q.-K., Sang, H.-Y., Duan, J.-H., & Gao, L. (2014). An improved fruit fly optimization algorithm for continuous function optimization problems. Knowledge-Based Systems, 62, 69–83.CrossRefGoogle Scholar
  25. 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
  26. Rahimi-Vahed, A. (2007). A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Computers & Industrial Engineering, 53(4), 642–666.CrossRefGoogle Scholar
  27. Rahimi-Vahed, A., & Mirzaei, A. H. (2007). Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. Soft Computing, 12(5), 435–452.CrossRefGoogle Scholar
  28. 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
  29. 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
  30. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.CrossRefGoogle Scholar
  31. 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
  32. Seçkiner, S. U., Eroğlu, Y., Emrullah, M., & Dereli, T. (2013). Ant colony optimization for continuous functions by using novel pheromone updating. Applied Mathematics and Computation, 219(9), 4163–4175.CrossRefGoogle Scholar
  33. 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
  34. Tanweer, M. R., Suresh, S., & Sundararajan, N. (2015). Self regulating particle swarm optimization algorithm. Information Sciences, 294, 182–202.CrossRefGoogle Scholar
  35. Tarique, A., & Gabbar, H. A. (2013). Particle swarm optimization (PSO) based turbine control. Intelligent Control and Automation, 4, 126–137.CrossRefGoogle Scholar
  36. Vijay Chakaravarthy, G., Marimuthu, S., & Naveen Sait, A. (2013). Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. Journal of Intelligent Manufacturing, 24, 175–191.CrossRefGoogle Scholar
  37. 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
  38. 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.
  39. 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.
  40. Zhang, X., Xu, T., Zhao, L., Fan, H., & Zang, J. (2015). Research on flatness intelligent control via GA-PIDNN. Journal of Intelligent Manufacturing, 26, 359–367.CrossRefGoogle Scholar
  41. Zhang, X., Zhang, Y., Shi, Y., et al. (2012). Power control algorithm in cognitive radio system based on modified shuffled frog leaping algorithm. International Journal of Electronics and Communications, 66(6), 448–454.CrossRefGoogle Scholar
  42. Zou, F., Wang, L., Hei, X., et al. (2014). Teaching–learning-based optimization with dynamic group strategy for global optimization. Information Sciences, 273, 112–131.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Key Lab of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdaoChina
  2. 2.National Engineering Research Center for Equipment and Technology of Cold Strip RollingQinhuangdaoChina
  3. 3.Qinhuangdao Institute of TechnologyQinhuangdaoChina

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