Clustering Analysis of the Population in Db_SHADE Algorithm

  • Adam Viktorin
  • Roman Senkerik
  • Michal Pluhacek
  • Tomas Kadavy
Keywords: Distance based parameter adaptation, SHADE, Differential evolution, DBSCAN

Abstract

This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion.

References

Storn, R., Price, K.: Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI, Berkeley (1995)

Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis, Artificial Intelligence Review, 33(1-2), 61-106 (2010)

Das, S., Suganthan, P. N.: Differential evolution: a survey of the state-of-the-art. IEEE transactions on evolutionary computation, 15(1), 4-31 (2011)

Das, S., Mullick, S. S., Suganthan, P. N.: Recent advances in differential evolution–An updated survey. Swarm and Evolutionary Computation, 27, 1-30 (2016)

Gämperle, R., Müller, S. D., Koumoutsakos, P.: A parameter study for differential evolution. Advances in intelligent systems, fuzzy systems, evolutionary computation, 10, 293-298 (2002)

Liu, J., Lampinen, J.: On setting the control parameter of the differential evolution method. In: Proceedings of 8th International Conference on Soft Computing – MENDEL 2002, no. 8 in MENDEL, pp. 11-18. Brno University of Technology, VUT press, Brno (2002)

Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: Evolutionary Computation (CEC), 2013 IEEE Congress on, pp. 71-78. IEEE (2013)

Tanabe, R., Fukunaga, A. S.: Improving the search performance of SHADE using linear population size reduction. In: Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 1658-1665. IEEE (2014)

Guo, S. M., Tsai, J. S. H., Yang, C. C., Hsu, P. H.: A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1003-1010. IEEE (2015)

Awad, N. H., Ali, M. Z., Suganthan, P. N., Reynolds, R. G.: An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: Evolutionary Computation (CEC), 2016 IEEE Congress on, pp. 2958-2965. IEEE (2016)

Brest, J., Maučec, M. S., Bošković, B.: Single objective real-parameter optimization: Algorithm jSO. In: Evolutionary Computation (CEC), 2017 IEEE Congress on, pp. 1311-1318. IEEE (2017)

Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T. Zamuda, A.: Distance Based Parameter Adaptation for Differential Evolution. In: Computational Intelligence (SSCI), 2017 IEEE Symposium Series on, pp. 1-7 IEEE (2017)

Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T.: SHADE Algorithm Dynamic Analyzed Through Complex Network. In: International Computing and Combinatorics Conference, pp. 666-677. Springer, Cham (2017)

Viktorin, A., Pluhacek, M., Senkerik, R., Kadavy, T.: Detecting Potential Design Weaknesses in SHADE Through Network Feature Analysis. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 662-673. Springer, Cham (2017)

Senkerik, R., Viktorin, A., Pluhacek, M.: On the transforming of the indices selection mechanism inside differential evolution into complex network. In: Intelligent Networking and Collaborative Systems (INCoS), 2016 International Conference on, pp. 186-192. IEEE (2016)

Viktorin, A., Pluhacek, M., Senkerik, R.: Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. In: Evolutionary Computation (CEC), 2016 IEEE Congress on, pp. 4797-4803. IEEE (2016)

Senkerik, R., Pluhacek, M., Viktorin, A., Kadavy, T.: On the Randomization of Indices Selection for Differential Evolution. In: Computer Science On-line Conference, pp. 537-547. Springer, Cham (2017)

Zhang, J., Sanderson, A. C.: JADE: adaptive differential evolution with optional external archive. Evolutionary Computation, IEEE Transactions on, 13(5), 945-958 (2009)

Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, 96(34), 226-231 (1996)

Deza, M. M., Deza, E.: Encyclopedia of distances. In: Encyclopedia of Distances. Springer, Berlin, Heidelberg (2009)

Published
2018-06-01
How to Cite
[1]
ViktorinA., SenkerikR., PluhacekM. and KadavyT. 2018. Clustering Analysis of the Population in Db_SHADE Algorithm. MENDEL. 24, 1 (Jun. 2018), 9-16. DOI:https://doi.org/10.13164/mendel.2018.1.009.
Section
Articles