Abstract
Bat algorithm(BA) is a new swarm intelligence optimization algorithm. However, bat algorithm has the obvious phenomenon of the premature convergence problem and is easily trapped into local optimum. In order to overcome the shortcoming of the BA algorithm, we proposed an improved bat algorithm called bat algorithm with recollection(RBA). Experiment were conducted on some benchmark functions. The experimental results show that the RBA can effectively avoid the premature convergence problem and has a good performance of global convergence property.
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
Kennedy, J., Eberhort, R.: Particle swarm optimization. In: Perth: IEEE International Conference on Neural networks, pp. 1941–1948 (1995)
Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A Novel Meta-heuristic Optimization Algorithm Inspired by Group Hunt-ing of Animals: Hunting Search. Computers & Mathematics with Applications 60(7), 2087–2098 (2010)
He, S., Wu, Q.H., Saunders, J.R.: A group search optimizer for neural network training. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3982, pp. 934–943. Springer, Heidelberg (2006)
Lemasson, B.H., Goodwin, R.A.: Collective motion in animal groups from a neurobiological perspective: the adaptive benefits of dynamic sensory loads and selective attention. Journal of Theoretical Biology 261(4), 501–510 (2009)
Dorigo, M., Maniezzo, V., Coloria, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics: PartB 26(1), 29–41 (1996)
Jiao, L.C., Wang, L.: Anovel genetic algorithm based on immunity. IEEE Transaction on System, Man and Cybernetic 30(5), 552–561 (2000)
Li, X.-L., Qian, J.-X.: An optimizing method based on autonomous animals: fish-swarm algorithm. Systems engineering theory and Practice 22(11), 32–38 (2002)
Huang, D.-S., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds.): ICIC 2010. LNCS, vol. 6216. Springer, Heidelberg (2010)
Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodel functions with collective robotics applications. Multiagent and Grid Systems 2(3), 209–222 (2006)
Chen, J.-R., Wang, Y.: Using fishing strategy optimization method. Computer engineering and Applications 45(9), 53–56 (2009)
Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications, India (2009)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, W., Wang, Y., Wang, X. (2013). Bat Algorithm with Recollection. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-39482-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
eBook Packages: Computer ScienceComputer Science (R0)