Abstract
Nature inspired algorithms have gained increasing attention as a powerful technique for solving optimization problems. Dynamic virtual bats algorithm (DVBA) is a relatively new nature inspired optimization algorithm. DVBA, like Bat Algorithm (BA) , is fundamentally inspired by bat’s hunting strategies, but it is conceptually very different from BA. In DVBA, a role based search is developed to avoid deficiencies of BA. Although the new technique outperforms BA significantly, there is still an insufficiency in DVBA regarding its exploration, when it comes to high dimensional complex optimization problems. To increase the performance of DVBA, this paper presents a novel, improved dynamic virtual bats algorithm (IDVBA) based on probabilistic selection. The performance of the proposed IDVBA is compared with seven meta-heuristic algorithms on a suite of 30 bound-constrained optimization problems from CEC 2014. The experimental results demonstrated that the proposed IDVBA outperform, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates for high dimensional problems.
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 subscriptionsReferences
M. Airas. Echolocation in bats, in Proceedings of Spatial Sound Perception and Reproduction. The Postgrad Seminar Course of HUT Acoustics Laboratory (2003), pp. 1–25
C. Chandrasekar et al. An optimized approach of modified bat algorithm to record deduplication. Int. J. Comput. Appl. 62(1) (2013)
S. Das, A. Abraham, U.K. Chakraborty, A. Konar, Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)
D. Handayani, N. Nuraini, O. Tse, R. Saragih, J. Naiborhu, Convergence analysis of particle swarm optimization (pso) method on the with-in host dengue infection treatment model, in AIP Conference Proceedings, vol. 1723 (AIP Publishing, 2016), p. 030013
J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (U Michigan Press, 1975)
T. Huang, A.S. Mohan, Micro-particle swarm optimizer for solving high dimensional optimization problems (\(\mu \)pso for high dimensional optimization problems). Appl. Math. Comput. 181(2), 1148–1154 (2006)
L. Jakobsen, A. Surlykke, Vespertilionid bats control the width of their biosonar sound beam dynamically during prey pursuit. Proc. Nat. Acad. Sci. 107(31), 13930–13935 (2010)
M.W.U. Kabir, N. Sakib, S.M.R. Chowdhury, M.S. Alam. A novel adaptive bat algorithm to control explorations and exploitations for continuous optimization problems. Int. J. Comput. Appl. 94(13) (2014)
A. Kaveh, P. Zakian, Enhanced bat algorithm for optimal design of skeletal structures. Asian J. Civial Eng. 15(2), 179–212 (2014)
J. Kennedy, C. Eberhart, iparticle swarm optimization, in Proceedings of IEEE International Conference on Neural Network (1995), 1942Y1948–1995
M. Koppen, K. Franke, R. Vicente-Garcia, Tiny gas for image processing applications. Comput. Intell. Mag. IEEE 1(2), 17–26 (2006)
J.-H. Lin, C.-W. Chou, C.-H. Yang, H.-L. Tsai, A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Comput. Inf. Technol. 2(2), 56–63 (2012)
K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control. Control Syst. IEEE 22(3), 52–67 (2002)
B. Ramesh, V.C.J. Mohan, V.V. Reddy, Application of bat algorithm for combined economic load and emission dispatch. Int. J. Electr. Eng. Telecomm. 2(1), 1–9 (2013)
I. Rechenberg, Evolutionsstrategie 94, in Werkstatt Bionik und Evolutionstechnik, vol. 1 (Frommann-Holzboog, Stuttgart, 1994)
A.O. Topal, O. Altun, Dynamic virtual bats algorithm (dvba) for global numerical optimization, in 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS) (IEEE, 2014), pp. 320–327
A.O. Topal, O. Altun, A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf. Sci. 354, 222–235 (2016)
A.O. Topal, O. Altun, E. Terolli, Dynamic virtual bats algorithm (dvba) for minimization of supply chain cost with embedded risk, in Proceedings of the 2014 European Modelling Symposium (IEEE Computer Society, 2014), pp. 58–64
A.O. Topal, Y.E. Yildiz, M. Ozkul, Improved dynamic virtual bats algorithm for global numerical optimization, in Lecture Notes in Engineering and Computer Science: Proceedings of the World Congress on Engineering and Computer Science, 25–27 Oct 2017 (San Francisco, USA, 2017), pp. 462–467
F. Van den Bergh, A.P. Engelbrecht, A cooperative approach to particle swarm optimization. IEEE Trans. Evolut. Comput. 8(3), 225–239 (2004)
X. Wang, W. Wang, Y. Wang, An adaptive bat algorithm, in Intelligent Computing Theories and Technology (Springer, 2013), pp. 216–223
X.-S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Springer, 2010), pp. 65–74
X.-S. Yang, A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)
Y.E. Yldz, O. Altun, Hybrid achievement oriented computational chemotaxis in bacterial foraging optimization: a comparative study on numerical benchmark. Soft Comput. 1–17 (2015)
M. Yuanbin, Z. Xinquan, X. Shujian, Local memory search bat algorithm for grey economic dynamic system. TELKOMNIKA Ind. J. Electr. Eng. 11(9), 4925–4934 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Topal, A.O., Yildiz, Y.E., Ozkul, M. (2019). Dynamic Virtual Bats Algorithm with Probabilistic Selection Restart Technique. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-2191-7_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-2191-7_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2190-0
Online ISBN: 978-981-13-2191-7
eBook Packages: EngineeringEngineering (R0)