Abstract
Opposition-based learning (OBL) scheme is an effective mechanism to enhance soft computing techniques, but it also has some limitations. To extend the OBL scheme, this paper proposes a novel rotation-based learning (RBL) mechanism, in which a rotation number is achieved by applying a specified rotation angle to the original number along a specific circle in two-dimensional space. By assigning different angles, RBL can search any point in the search space. Therefore, RBL could be more flexible than OBL to find the promising candidate solutions in the complex search spaces. In order to verify its effectiveness, the RBL mechanism is embedded into differential evolution (DE) and the rotation-based differential evolution (RDE) algorithm is introduced. Experimental studies are conducted on a set of widely used benchmark functions. Simulation results demonstrate the effectiveness of RBL mechanism, and the proposed RDE algorithm performs significantly better than, or at least comparable to, several state-of-the-art DE variants.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brest, J., Greiner, S., Bošković, B., et al.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
El-Abd, M.: Generalized opposition-based artificial bee colony algorithm. In: IEEE Cong. Evol. Compu., pp. 1–4. IEEE (June 2012)
Jabeen, H., Jalil, Z., Baig, A.R.: Opposition based initialization in particle swarm optimization (O-PSO). In: Proc. of the 11th Annual Conf. Comp. on Genetic and Evol. Comput., GECCO 2009, pp. 2047–2052. ACM (July 2009)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Quasi-oppositional differential evolution. In: IEEE Cong. Evol. Compu., pp. 2229–2236. IEEE (September 2007)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)
Rahnamayan, S., Wang, G.G., Ventresca, M.: An intuitive distance-based explanation of opposition-based sampling. Applied Soft Computing 12(9), 2828–2839 (2012)
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, International Computer Science Institute, Berkeley, CA (March 1995)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Inter. Conf. Comput. Intell. for Model., Control and Auto., and Inter. Conf. Intell. Agents, Web Tech. and Inter. Commerce, vol. 1, pp. 695–701. IEEE (November 2005)
Wang, H., Rahnamayan, S., Sun, H., Omran, M.G.: Gaussian bare-bones differential evolution. IEEE Trans. Cyber. 43(2), 634–647 (2013)
Wang, H., Rahnamayan, S., Wu, Z.: Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. Journal of Parallel and Distributed Computing 73(1), 62–73 (2013)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)
Xu, Q., Wang, L., Wang, N., Hei, X., Zhao, L.: A review of opposition-based learning from 2005 to 2012. Engineering Applications of Artificial Intelligence 29(1), 1–12 (2014)
Yang, X., Huang, Z.: Opposition-based artificial bee colony with dynamic cauchy mutation for function optimization. International Journal of Advancements in Computing Technology 4(4), 56–62 (2012)
Zhang, J., Sanderson, A.C.: JADE: Self-adaptive differential evolution with fast and reliable convergence performance. In: IEEE Cong. Evol. Compu., pp. 2251–2258. IEEE (September 2007)
Zhou, X., Wu, Z., Wang, H., Li, K.: Elite opposition-based particle swarm optimization. Acta Electronica Sinica 41(8), 1647–1652 (2013) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, H., Wu, Z., Li, H., Wang, H., Rahnamayan, S., Deng, C. (2014). Rotation-Based Learning: A Novel Extension of Opposition-Based Learning. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)