Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 1395025, 12 pages
https://doi.org/10.1155/2017/1395025
Research Article

Memetic Differential Evolution with an Improved Contraction Criterion

1School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Hongshan District, Wuhan, China
2Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China

Correspondence should be addressed to Lei Peng; nc.ude.guc@gnep.iel

Received 1 November 2016; Revised 4 March 2017; Accepted 13 March 2017; Published 4 April 2017

Academic Editor: Manuel Graña

Copyright © 2017 Lei Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Moscato, “On evolution, search, optimization, genetic algorithms and martial arts—towards memetic algorithms,” Technical Report Caltech Concurrent Computation Program 826, California Institute of Technology, Pasadena, Calif, USA, 1989. View at Google Scholar
  2. M. A. Ahandani, M.-T. Vakil-Baghmisheh, and M. Talebi, “Hybridizing local search algorithms for global optimization,” Computational Optimization and Applications, vol. 59, no. 3, pp. 725–748, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. D. Molina, M. Lozano, C. García-Martínez, and F. Herrera, “Memetic algorithms for continuous optimisation based on local search chains,” Evolutionary Computation, vol. 18, no. 1, pp. 27–63, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Simon, M. G. Omran, and M. Clerc, “Linearized biogeography-based optimization with re-initialization and local search,” Information Sciences, vol. 267, pp. 140–157, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. J. Sun, J. M. Garibaldi, N. Krasnogor, and Q. Zhang, “An intelligent multi-restart memetic algorithm for box constrained global optimisation,” Evolutionary Computation, vol. 21, no. 1, pp. 107–147, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Wang, I. Moon, S. Yang, and D. Wang, “A memetic particle swarm optimization algorithm for multimodal optimization problems,” Information Sciences, vol. 197, pp. 38–52, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Di Carlo, M. Vasile, and E. Minisci, “Multi-population adaptive inflationary differential evolution,” in Proceedings of the Student Workshop on Bio-inspired Optimization Methods and their Applications (BIOMA '14), pp. 41–54, 2014. View at Scopus
  9. E. Minisci and M. Vasile, “Adaptive inflationary differential evolution,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '14), pp. 1792–1799, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Vasile, E. Minisci, and M. Locatelli, “An inflationary differential evolution algorithm for space trajectory optimization,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 2, pp. 267–281, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Glotić and A. Zamuda, “Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution,” Applied Energy, vol. 141, pp. 42–56, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Zamuda and J. D. Hernández Sosa, “Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures,” Applied Soft Computing Journal, vol. 24, pp. 95–108, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Mingyong and C. Erbao, “An improved differential evolution algorithm for vehicle routing problem with simultaneous pickups and deliveries and time windows,” Engineering Applications of Artificial Intelligence, vol. 23, no. 2, pp. 188–195, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Locatelli and M. Vasile, “(Non) convergence results for the differential evolution method,” Optimization Letters, vol. 9, no. 3, pp. 413–425, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. L. dos Santos Coelho and V. C. Mariani, “Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 989–996, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 107–125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Muelas, A. LaTorre, and J.-M. Peña, “A memetic differential evolution algorithm for continuous optimization,” in Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA '09), pp. 1080–1084, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Neri and V. Tirronen, “Scale factor local search in differential evolution,” Memetic Computing, vol. 1, no. 2, pp. 153–171, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Wang, B. Li, and Z. He, “Enhancing differential evolution with effective evolutionary local search in memetic framework,” in Proceedings of the IEEE Congress of Evolutionary Computation (CEC '11), pp. 2457–2464, IEEE, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), vol. 2, pp. 1785–1791, IEEE, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evolutionary Computation, vol. 9, no. 2, pp. 159–195, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Wang and B. Li, “A self-adaptive mixed distribution based uni-variate estimation of distribution algorithm for large scale global optimization,” in Nature-Inspired Algorithms for Optimisation, vol. 193 of Studies in Computational Intelligence, pp. 171–198, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  23. M. Locatelli, M. Maischberger, and F. Schoen, “Differential evolution methods based on local searches,” Computers and Operations Research, vol. 43, no. 1, pp. 169–180, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Asafuddoula, T. Ray, and R. Sarker, “An adaptive hybrid differential evolution algorithm for single objective optimization,” Applied Mathematics and Computation, vol. 231, pp. 601–618, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. A. K. Qin, K. Tang, H. Pan, and S. Xia, “Self-adaptive differential evolution with local search chains for real-parameter single-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '14), pp. 467–474, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Trivedi, D. Srinivasan, S. Biswas, and T. Reindl, “Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem,” Swarm and Evolutionary Computation, vol. 23, pp. 50–64, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. Y.-L. Li, Z.-H. Zhan, Y.-J. Gong, W.-N. Chen, J. Zhang, and Y. Li, “Differential evolution with an evolution path: a deep evolutionary algorithm,” IEEE Transactions on Cybernetics, vol. 45, no. 9, pp. 1798–1810, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Sun and Z.-Q. Wu, “Fractal and multifractal description of surface topography,” Acta Physica Sinica, vol. 50, no. 11, pp. 2130–2131, 2001. View at Google Scholar · View at Scopus
  29. R. Ros, “Benchmarking the bfgs algorithm on the bbob-2009 function testbed,” in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2409–2414, 2009.
  30. A. Zamuda, J. Brest, and E. Mezura-Montes, “Structured population size reduction differential evolution with multiple mutation strategies on CEC 2013 real parameter optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '13), pp. 1925–1931, IEEE, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. P. N. Suganthan, “Problem definitions and evaluation criteria for the cec2005 special session on real-parameter optimization,” Tech. Rep., Nanyang Technological University, Singapore, 2005. View at Google Scholar
  32. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. C. García-Martínez, M. Lozano, F. Herrera, D. Molina, and A. M. Sánchez, “Global and local real-coded genetic algorithms based on parent-centric crossover operators,” European Journal of Operational Research, vol. 185, no. 3, pp. 1088–1113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Tanabe and A. S. Fukunaga, “Improving the search performance of shade using linear population size reduction,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '14), pp. 1658–1665, IEEE, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. S. García, A. Fernández, J. Luengo, and F. Herrera, “A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability,” Soft Computing, vol. 13, no. 10, pp. 959–977, 2009. View at Publisher · View at Google Scholar · View at Scopus
  36. S. García, D. Molina, M. Lozano, and F. Herrera, “A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization,” Journal of Heuristics, vol. 15, no. 6, pp. 617–644, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Alcalá-Fdez, L. Sánchez, S. García et al., “KEEL: a software tool to assess evolutionary algorithms for data mining problems,” Soft Computing, vol. 13, no. 3, pp. 307–318, 2009. View at Publisher · View at Google Scholar · View at Scopus