Advertisement

Progress in Artificial Intelligence

, Volume 5, Issue 2, pp 85–89 | Cite as

Evolutionary algorithms for large-scale global optimisation: a snapshot, trends and challenges

  • Daniel Molina Cabrera
Regular Paper

Abstract

In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge increase of the domain search due to the dimensionality. Large-scale global optimisation is a research area getting more attention in the last years, thus many algorithms, mainly evolutionary algorithms, have been specially designed to tackle it. In this paper, we give a brief introduction of several of them and their techniques, remarking techniques based on grouping of variables and memetic algorithms, because they are two promising approaches. Also, we have reviewed the winners of the different competitions in the area, to give a snapshot of the algorithms that have obtained the best results in this area. Finally, several interesting trends in the research area have been pointed out, and some future trends and challenges have been suggested.

Keywords

Large-scale global optimisation Large scale High-dimensional problems Real-coding optimisation Evolutionary algorithms 

Notes

Acknowledgments

This work was supported by the Spanish Ministry of Education and Science under Grants TIN2012-37930-C02-01, TIN2014-57251-P and Research Regional Projects P10-TIC-6858, P12-TIC-2958.

References

  1. 1.
    Ali, A., Hassanien, A., Snášel, V.: The nelder-mead simplex method with variables partitioning for solving large scale optimization problems. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol. 237, pp. 271–284. Springer International Publishing (2014)Google Scholar
  2. 2.
    Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol (1997)zbMATHGoogle Scholar
  3. 3.
    van den Bergh, F., Engelbrecht, A.: A cooperative approach to particle swarm optimization. IEEE Trans. Evolut. Comput. 8(3), 225–239 (2004)CrossRefGoogle Scholar
  4. 4.
    Brest, J., Zamuda, A., Fister, I., Maučec, M.: Large scale global optimization using self-adaptive differential evolution algorithm. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  5. 5.
    Cao, Y., Sun, D.: A parallel computing framework for large-scale air traffic flow optimization. IEEE Trans. Intell. Transp. Syst. 13(4), 1855–1864 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 1(11), 1–18 (2003)CrossRefGoogle Scholar
  7. 7.
    Korosec, P., Tashkova, K., Silc, J.: The differential ant-stigmergy algorithm for large-scale global optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  8. 8.
    LaTorre, A., Muelas, S., Peña, J.M.: A comprehensive comparison of large scale global optimizers. Inf. Sci. 316, 517–549 (2015)CrossRefGoogle Scholar
  9. 9.
    LaTorre, A., Muelas, S., Pena, J.M.: Large scale global optimization: Experimental results with mos-based hybrid algorithms. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2742–2749 (2013)Google Scholar
  10. 10.
    Li, X., Tang, K., Omidvar, M., Yang, Z., Qin, K., Tang, K.: Benchmark functions for the CEC’2013 special session and competition on large scale global optimization. Tech. rep., Evolutionary Computation and Machine Learning Group, RMIT University, Australia (2013)Google Scholar
  11. 11.
    Li, X., Tang, K., Suganthan, P., Yang, Z.: Editorial for the special issue of Information Sciences Journal (ISJ) on nature-inspired algorithms for large scale global optimization. Inf. Sci. 316, 437–439 (2015)CrossRefGoogle Scholar
  12. 12.
    Liao, T., Molina, D., Stützle, T.: Performance evaluation of automatically tuned continuous optimizers on different benchmark sets. Appl. Soft Comput. 27, 490–503 (2015)CrossRefGoogle Scholar
  13. 13.
    Liu, J., Tang, K.: Scaling up covariance matrix adaptation evolution strategy using cooperative coevolution. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) Intelligent Data Engineering and Automated Learning IDEAL 2013. Lecture Notes in Computer Science, vol. 8206, pp. 350–357. Springer Berlin Heidelberg (2013)Google Scholar
  14. 14.
    Lozano, M., Molina, D., Herrera, F.: Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 15(11), 2085–2087 (2011)CrossRefGoogle Scholar
  15. 15.
    Molina, D., Herrera, F.: Iterative hybridization of de with local search for the cec’2015 special session on large scale global optimization. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1974–1978 (2015)Google Scholar
  16. 16.
    Molina, D., Lozano, M., Herrera, F.: MA-SW-Chains: memetic algorithm based on local search chains for large scale continuous global optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  17. 17.
    Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Toward Memetic Algorithms. Tech. rep., Caltech Concurrent Computation Program. California Institute of Technology, Pasaden (1989)Google Scholar
  18. 18.
    Neri, F., Cotta, C.: Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol. Comput. 2, 1–14 (2012)CrossRefGoogle Scholar
  19. 19.
    Omidvar, M., Li, X., Mei, Y., Yao, X.: Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2014)CrossRefGoogle Scholar
  20. 20.
    Omidvar, M., Mei, Y., Li, X.: Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1305–1312 (2014)Google Scholar
  21. 21.
    Omidvar, M.N., Li, X., Tang, K.: Designing benchmark problems for large-scale continuous optimization. Inf. Sci. 316, 419–436 (2015)CrossRefGoogle Scholar
  22. 22.
    Ren, Y., Wu, Y.: An efficient algorithm for high-dimensional function optimization. Soft Comput. 17(6), 995–1004 (2013)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Shi, Y., Zhang, J., O’Donoghue, B., Letaief, K.: Large-scale convex optimization for dense wireless cooperative networks. IEEE Trans. Signal Process. 63(18), 4729–4743 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Sun, L., Yoshida, S., Cheng, X., Liang, Y.: A cooperative particle swarm optimizer with statistical variable interdependence learning. Inf. Sci. 186(1), 20–39 (2012)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Tech. rep., Nature Inspired Computation and Applications Laboratory (2009)Google Scholar
  26. 26.
    Tseng, L.Y., Chen, C.: Multiple trajectory search for large scale global optimization. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp. 3052–3059 (2008)Google Scholar
  27. 27.
    Wang, Y., Li, B.: Two-stage based ensemble optimization for large-scale global optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  28. 28.
    Wang, Y., Member, S., Li, B.: A restart univariate estimation of distribution algorithm: sampling under mixed gaussian and lévy probability distribution. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2008), Hongkong, pp. 3218–3925 (2008)Google Scholar
  29. 29.
    Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  30. 30.
    Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Yang, Z., Tang, K., Yao, X.: Multilevel cooperative coevolution for large scale optimization. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp. 1663–1670 (2008)Google Scholar
  32. 32.
    Yang, Z., Zhang, J., Tang, K., Yao, X., Sanderson, A.: An adaptive coevolutionary differential evolution algorithm for large-scale optimization. In: IEEE Congress on Evolutionary Computation, 2009. CEC ’09, pp. 102–109 (2009)Google Scholar
  33. 33.
    Zhao, S., Liang, J., Suganthan, P., Tasgetiren, M.: Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pp. 3845–3852 (2008)Google Scholar
  34. 34.
    Zhao, S.Z., Suganthan, P., Das, S.: Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  35. 35.
    Zhao, S.Z., Suganthan, P., Das, S.: Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Comput. 15(11), 2175–2185 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CádizCádizSpain

Personalised recommendations