Abstract
Artificial bee colony (ABC) algorithm is relatively a new bio-inspired swarm intelligence optimization technique comparative to other population based algorithms. In this study BGA (breeder GA) mutation is embedded into onlooker bee phase to improve the capability of local search. The proposed variant is named B-ABC. The experimental results on 10 constrained benchmark functions demonstrate the performance of the proposed variant against those of state-of-the-art algorithms for a set of constrained test problems. Further the efficiency of the proposed variant is tested on the car side impact problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through a simulation of evolution. In: Maxfield, M., Callahan, Fogel, L.J. (eds.) Biophysics and Cybernetic Systems, Proceeding of the 2nd Cybernetic Sciences Symposium, pp. 131–155 (1965)
Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley Publishing Company, Reading (1986)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Price, K., Storn, R.: Differential Evolution – a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, Technical Report, International Computer Science Institute, Berkley (1995)
Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy, Technical Report 91-016, Politecnico di Milano, Italy (1991)
Karaboga, D.: An Idea based on Bee Swarm for Numerical Optimization, Technical Report, TR-06, Erciyes University Engineering Faculty, Computer Engineering Department (2005)
Karaboga, D., Basturk, B.: A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization 39, 459–471 (2007)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8, 687–697 (2008)
Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. (2011), doi:10.1007/s10462-012-9328-0
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Engrg. 186, 311–338 (2000)
Zhu, G.P., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation (2010), doi:10.1016/j.amc.2010.08.049
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm I: Continuous parameter optimization. Evol. Comput. 1(1), 25–49 (1993)
Gu, L., Yang, R.J., Cho, C.H., Makowski, M., Faruque, M., Li, Y.: Optimization and robustness for crashworthiness. Int. J. Vehicle Des. 26(4), 348–360 (2001)
Youn, B.D., Choi, K.K.: A new response surface methodology for reliability-based design optimization. Comput. Struct. 82, 241–256 (2004)
Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello Coello, C.A., Deb, K.: Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization, Technical Report (September 2006), http://www.lania.mx/~emezura/documentos/tr_cec06.pdf
Koziel, S., Michalewicz, Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation 7(1), 19–44 (1999)
Hamida, S.B., Schoenauer, M.: ASCHEA: new results using adaptive segregational constraint handling. In: Proceedings of the Congress on Evolutionary Computation 2002 (CEC 2002), vol. 1, pp. 884–889. IEEE Service Center, Piscataway (2002)
Runarsson, T.P., Yao, X.: Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(2), 233–243 (2005)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation 4(3), 284–294 (2000)
Mezura-Montes, E., Coello Coello, C.A.: A simple multimembered evolution strategy to solve constrained optimization problems. Technical Report EVOCINV-04–2003
Muñoz-Zavala, A., Hernández, A.A., Diharce, E.R.V.: Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), New York, vol. 1, pp. 209–216. ACM Press, Washington, DC (2005) ISBN 1–59593-010–8
Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Mixed variable structural optimization using Firefly Algorithm. Computers and Structures 89, 2325–2336 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sharma, T.K., Pant, M., Singh, V.P. (2012). Modified Onlooker Phase in Artificial Bee Colony Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-35380-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35379-6
Online ISBN: 978-3-642-35380-2
eBook Packages: Computer ScienceComputer Science (R0)