Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 pp 591-601 | Cite as
Preliminary Study on the Randomization and Sequencing for the Chaos Embedded Heuristic
Abstract
This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper investigates the utilization of the time-continuous chaotic system, which is UEDA oscillator, as the chaotic pseudo random number generator. (CPRNG). Repeated simulations were performed investigating the influence of the oscillator sampling time to the selected heuristic, which is differential evolution algorithm (DE). Through the utilization of time-continuous systems and with different sampling times from very small to bigger, it is possible to fully keep, suppress or remove the hidden complex chaotic dynamics from the generated data series. Experiments are focused on the preliminary investigation, whether the different randomization given by particular CPRNG or hidden complex chaotic dynamics providing the unique sequencing are beneficial to the heuristic performance. Initial experiments were performed on the selected test function in several dimension settings.
Keywords
Differential evolution Complex dynamics Deterministic chaos Randomization UEDA oscillatorNotes
Acknowledgments
This work was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant of SGS No. SP2015/142 and SP2015/141 of VSB—Technical University of Ostrava, Czech Republic and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/FAI/2015/057 and IGA/FAI/2015/061.
References
- 1.Price, K.V.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill Ltd. (1999)Google Scholar
- 2.Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011). doi: 10.1109/TEVC.2010.2059031 CrossRefGoogle Scholar
- 3.Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)CrossRefGoogle Scholar
- 4.Weber, M., Neri, F., Tirronen, V.: A study on scale factor in distributed differential evolution. Inf. Sci. 181(12), 2488–2511 (2011)CrossRefGoogle Scholar
- 5.Neri, F., Iacca, G., Mininno, E.: Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf. Sci. 181(12), 2469–2487 (2011)MathSciNetCrossRefGoogle Scholar
- 6.Iacca, G., Caraffini, F., Neri, F.: Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead. J. Comput. Sci. Technol. 27(5), 1056–1076 (2012)MathSciNetCrossRefMATHGoogle Scholar
- 7.Aydin, I., Karakose, M., Akin, E.: Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection. Expert Syst. Appl. 37(7), 5285–5294 (2010)CrossRefGoogle Scholar
- 8.Liang, W., Zhang, L., Wang, M.: The chaos differential evolution optimization algorithm and its application to support vector regression machine. J. Softw. 6(7), 1297–1304 (2011)CrossRefGoogle Scholar
- 9.Zhenyu, G., Bo, C., Min, Y., Binggang, C.: Self-adaptive chaos differential evolution. In: Jiao, L., Wang, L., Gao, X.-B., Liu, J., Wu, F. (eds.) Advances in Natural Computation. Lecture Notes in Computer Science, vol. 4221, pp. 972–975. Springer, Berlin Heidelberg (2006)CrossRefGoogle Scholar
- 10.Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Comput. Math. Appl. 60(4), 1088–1104 (2010)MathSciNetCrossRefMATHGoogle Scholar
- 11.Coelho, L.D.S., Mariani, V.C.: A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch. Chaos, Solitons Fractals 39(2), 510–518 (2009)CrossRefGoogle Scholar
- 12.Pluhacek, M., Senkerik, R., Davendra, D., Kominkova Oplatkova, Z., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput. Math. Appl. 66(2), 122–134 (2013)MathSciNetCrossRefMATHGoogle Scholar
- 13.Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D.: Chaos PSO algorithm driven alternately by two different chaotic maps—an initial study. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2444–2449, 20–23 June 2013Google Scholar
- 14.Metlicka, M., Davendra, D.: Chaos-driven discrete artificial bee colony. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2947–2954 (2014)Google Scholar
- 15.Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)MathSciNetCrossRefMATHGoogle Scholar
- 16.Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution—A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin Heidelberg (2005)MATHGoogle Scholar
- 17.Bharti, L., Yuasa, M.: Energy variability and chaos in ueda oscillator. http://www.rist.kindai.ac.jp/no.23/yuasa-EVCUO.pdf
- 19.Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press (2003)Google Scholar