Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Micro Bat Algorithm for High Dimensional Optimization Problems

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 12
Year of Publication: 2015
Authors:
Ali Osman Topal
Oguz Altun
Yunus Emre Yildiz
10.5120/21749-5015

Ali Osman Topal, Oguz Altun and Yunus Emre Yildiz. Article: Micro Bat Algorithm for High Dimensional Optimization Problems. International Journal of Computer Applications 122(12):1-10, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Ali Osman Topal and Oguz Altun and Yunus Emre Yildiz},
	title = {Article: Micro Bat Algorithm for High Dimensional Optimization Problems},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {12},
	pages = {1-10},
	month = {July},
	note = {Full text available}
}

Abstract

Very recently bat inspired algorithms have gained increasing attention as a powerful technique for solving optimization problems. Bat algorithm (BA) is the first one in this group. It is based on the echolocation behavior of bats. BA is very good at exploitation however it is generally poor at exploration. Dynamic Virtual Bats Algorithm (DVBA) is another bat inspired algorithm, which is proposed lately. Although the algorithm is fundamentally inspired from BA, it is conceptually very different. DVBA employs just two bats and uses role based search mechanism. It is very efficient in exploration but relatively poor in exploitation, when it comes to high dimensional problems. In this paper, a novel micro-bat algorithm ( BA) is proposed which possess the advantages of both algorithms. BA employs a very small population compared to its classical version. It combines the swarming technique of bats in Bat Algorithm with the role based search in Dynamic Virtual Bats Algorithm. Our empirical results demonstrate that the proposed BA achieves a good balance between exploration and exploitation. And it exhibits a better overall performance than the standard BA with larger and smaller populations on high dimensional problems.

References

  • Xin-She Yang. A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010), pages 65–74. Springer, 2010.
  • Bandi Ramesh, V Chandra Jagan Mohan, and VC Veera Reddy. Application of bat algorithm for combined economic load and emission dispatch. Int. J. of Electricl Engineering and Telecommunications, 2(1):1–9, 2013.
  • A Kaveh and P Zakian. Enhanced bat algorithm for optimal design of skeletal structures. Asian J Civial Eng, 15(2):179– 212, 2014.
  • Mo Yuanbin, Zhao Xinquan, and Xiang Shujian. Local memory search bat algorithm for grey economic dynamic system. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(9):4925–4934, 2013.
  • Jiann-Horng Lin, Chao-Wei Chou, Chorng-Horng Yang, Hsien-Leing Tsai, et al. A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Computer and Information Technology, 2(2):56–63, 2012.
  • Xin-She Yang and Amir Hossein Gandomi. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5):464–483, 2012.
  • Mario K¨oppen, Katrin Franke, and Raul Vicente-Garcia. Tiny gas for image processing applications. Computational Intelligence Magazine, IEEE, 1(2):17–26, 2006.
  • Konstantinos E Parsopoulos. Cooperative microparticle swarm optimization. In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pages 467–474. ACM, 2009.
  • Sambarta Dasgupta, Arijit Biswas, Swagatam Das, Bijaya Ketan Panigrahi, and Ajith Abraham. A micro-bacterial foraging algorithm for high-dimensional optimization. In Evolutionary Computation, 2009. CEC'09. IEEE Congress on, pages 785– 792. IEEE, 2009.
  • Mauricio Olguin-Carbajal, Enrique Alba, and Javier Arellano-Verdejo. Micro-differential evolution with local search for high dimensional problems. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 48–54. IEEE, 2013.
  • Ali Osman Topal and Oguz Altun. Dynamic virtual bats algorithm (dvba) for global numerical optimization. In Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference on, pages 320–327. IEEE, 2014.
  • C Chandrasekar et al. An optimized approach of modified bat algorithm to record deduplication. International Journal of Computer Applications, 62(1), 2013.
  • Matti Airas. Echolocation in bats. In Proceedings of spatial sound perception and reproduction. The postgrad seminar course of HUT Acoustics Laboratory, pages 1–25, 2003.
  • Md Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, and Mohammad Shafiul Alam. A novel adaptive bat algorithm to control explorations and exploitations for continuous optimization problems. International Journal of Computer Applications, 94(13):15–20, 2014.
  • S Y?lmaz, E Ugur Kucuksille, and Y Cengiz. Modified bat algorithm. Elektronika ir Elektrotechnika, 20(2):71–78, 2014.
  • Ali Osman Topal, Oguz Altun, and Erisa Terolli. Dynamic virtual bats algorithm (dvba) for minimization of supply chain cost with embedded risk. In Proceedings of the 2014 European Modelling Symposium, pages 58–64. IEEE Computer Society, 2014.
  • Swagatam Das, Ajith Abraham, Uday K Chakraborty, and Amit Konar. Differential evolution using a neighborhoodbased mutation operator. Evolutionary Computation, IEEE Transactions on, 13(3):526–553, 2009.
  • JJ Liang, BY Qu, PN Suganthan, and Q Chen. Problem definitions and evaluation criteria for the cec 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2014.
  • A Kai Qin, Vicky Ling Huang, and Ponnuthurai N Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimization. Evolutionary Computation, IEEE Transactions on, 13(2):398–417, 2009.
  • Yun-Wei Shang and Yu-Huang Qiu. A note on the extended rosenbrock function. Evolutionary Computation, 14(1):119– 126, 2006.