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Now Vol.5 , Issue.9 September 2017 edition has been published. Authors are cordially invited to submit papers for the upcoming edition through online submission system

A Review on Bat Algorithm
A Review on Bat Algorithm
S.L. Yadav1 , M. Phogat2
1 Dept. of CSE, K. R Mangalam University, Gurugram, India.
2 Dept. of CSE, GJUST, Hisar, India .
Correspondence should be addressed to:

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 39-43, Jul-2017


Online published on Jul 30, 2017

Copyright © S.L. Yadav, M. Phogat . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: S.L. Yadav, M. Phogat, “A Review on Bat Algorithm”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.39-43, 2017.

MLA Style Citation: S.L. Yadav, M. Phogat "A Review on Bat Algorithm." International Journal of Computer Sciences and Engineering 5.7 (2017): 39-43.

APA Style Citation: S.L. Yadav, M. Phogat, (2017). A Review on Bat Algorithm. International Journal of Computer Sciences and Engineering, 5(7), 39-43.
Abstract :
Complications of cracking real world glitches with their promising difficulties forced computer technologist to search for more skillful problem solving approaches. Meta-heuristic procedures are outstanding models of these methods and out of these the bat algorithm (BA) is a good example. BAT algorithm is found very efficient in solving difficult problems. This algorithm has been advanced hurriedly and has been practical in different optimization jobs. The literature has extended substantially since last seven years. This paper offers appropriate study of the various modifications of BAT algorithm.
Key-Words / Index Term :
Artificial Bee Colony, Ant Colony Optimization, Bat Algorithm, Cuckoo Search Algorithm
References :
[1] X. Yang, “A New Metaheuristic Bat-Inspired Algorithm”, Proceedings of Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 65–74, 2010.
[2] B. Kumar, D. Kumar, “A Review on Artifical Bee Colony Algorithm”, International Journal of Engineering and Technology. Vol. 2, Issue 3, pp. 175-186, 2013.
[3] J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.
[4] M. Dorigo, M. Birattari and T. Stiitzle, “Ant Colony Optimization”, IEEE Computational Intelligence Magazine, pp. 28–39, 2006.
[5] R. Hedayatzadeh and A. Salmassi, “Termite Colony Optimization : A Novel Approach for Optimizing Continuous Problems”, Proceedings of Iranian Conference on Electrical Engineering (ICEE 2010), pp. 553-558, 2010.
[6] S. Chu, P. Tsai and J. Pan, “Cat Swarm Optimization”, Proceedings of Pacific Rim International Conference on Artificial Intelligence, pp. 854-858, 2006.
[7] W. Metzner, “Echolocation Behaviour in Bats”, Progress Edin-burgh, vol. 75, Issue 298, pp. 453-465, 1991.
[8] H. Schnitzler and E. Kalko, “Echolcation by insect eating bats”, Bioscience, vol. 51, Issue 7, pp. 557-569, 2001.
[9] K. Nikov, A. Nikov and A. Sahai, “A Fuzzy Bat Clustering Method for Ergonomic Screening of Office Workplaces”, Proceedings of Third International Conference on Software, Services and Semantic Technologies S3T, pp. 59-66, 2011.
[10] B. Mallikarjuna, K. Reddy and O. Hemakesaavulu, “Economic load dispatch problem with valve-point effect using a binary bat algorithm”, ACEEE International Journal of Elecrtical and Power Engineering, vol. 4, Issue 3, pp. 33-38, 2013.
[11] R. Nakamura, L. Pereira, K. Costa, D. Rodrigues, J. Papa and X. Yang, “BBA: A Binary Bat Algorithm for Feature Selection”, Proceedings of XXV SIBGRAPI Conference on Graphics, Patterns and Images, pp. 291-297, 2012.
[12] S. Sabba and S. Chikhi, “A discrete binary version of bat algorithm for multidimensional knapsack problem”, Int. J. Bio-Inspired Computation, vol. 6, Issue 2, pp. 140-152, 2014.
[13] J. Zhang and G. Wang, “Image Matching Using a Bat Algorithm with Mutation”, Applied Mechanics and Materials, vol. 203, Issue 2012, pp. 65-74, 2012.
[14] I. Fister, D. Fister and X. Yang, “A hybrid bat algorithm”, Elektrotehniski vestnik, in press, 2013.
[15] S. Saha, R. Kar, D. Mandal and S. Ghoshal, “A new design method using opposition-based BAT algorithm for IIR system identification problem”, International Journal of Bio-Inspired Computation, vol. 5, Issue 2, pp. 99-132, 2013.
[16] J. Xie, Y. Zhou and H. Chen, “A Novel Bat Algorithm Based on Differential Operator and Lévy Flights Trajectory”, Computational Intelligence and Neuroscience, pp. 1-13, 2013.
[17] H. Afrabandpey, M. Ghaffari, A. Mirzaei and M. Safayani, “A novel bat algorithm based on chaos for optimization tasks”, Proceedings of Intelligent Systems (ICIS), Iranian Conference, pp. 1-6, 2014.
[18] A. Gandomi and X. Yang, “Chaotic bat algorithm”, Journal of Computational Science, vol. 5, Issue 2, pp. 224-232, 2014.
[19] S. Yilmaz, E. Kucuksille and Y. Cengiz, “Modified Bat Algorithm”, Elektronika IR Elektrotechnika, vol. 20, Issue 2, pp. 71-78, 2014.
[20] L. Li and Y. Zhou, “A novel complex-valued bat algorithm”, Neural Computing and Applications, vol. 25, Issue 6, pp. 1369-1381, 2014.
[21] X. Cai, L. Wang, Q. Kang and Q. Wu, “Bat algorithm with Gaussian walk”, International Journal of Bio-Inspired Computation, vol. 6, Issue 3, pp. 166-174, 2014.
[22] Y. Zhou, J. Xie, L. Li, M. Ma, “Cloud Model Bat Algorithm”, The Scientific World Journal, pp. 1-11, 2014.
[23] D. Li, C. Liu and W. Gan, “Proof of the heavy-tailed property of normal cloud model”, Engineer and Science of China, vol. 13, Issue 4, pp. 20-23, 2011.
[24] T. Dao, J. Pan, T. Nguyen, S. Chu and C. Shieh, “Compact Bat Algorithm”, In: Intelligent Data analysis and its Applications. Volume II, Springer International Publishing: Cham, pp. 57-68, 2014.
[25] I. Fister, S. Fong, J. Brest and I. Fister, “Towards the Self-Adaption of the Bat Algorithm”, Proceddings of the IASTED International Conference Artificial Intelligence and Applications (AIA 2014), pp. 400-406, 2014.
[26] I. Fister, S. Fong, J. Brest and I. Fister, “A Novel Hybrid Self-Adaptive Bat Algorithm”, The Scientific World Journal, pp. 1-12,2014.
[27] S. Yilmaz and E. Küçüksille, “A new modification approach on bat algorithm for solving optimization problems”, Applied Soft Computing, vol. 28, pp. 259-275, 2015.
[28] A. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization”, Ecol. Inform., vol. 1, Issue 4, pp. 355-366, 2006.
[29] Jun L, Liheng L, and Xianyi W. A double-subpopulation variant of the bat algorithm. Applied Mathematics and Computation. 2015; 263:361-377.
[30] X. Meng, X. Gao, Y. Liu and H. Zhang, “A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization”, Expert Systems with Applications, vol. 42, Issue 17-18, pp. 6350-6364, 2015.
[31] G. Wang, H. Chu and S. Mirjalili, “Three-dimensional path planning for UCAV using an improved bat algorithm”, Aerospace Science and Technology, vol. 49, pp. 231-238, 2016.
[32] Y. Zhou, Q. Luo, J. Xie and H. Zheng, “A Hybrid Bat Algorithm with Path Relinking for the Capacitated Vehicle Routing Problem”, In: Metaheuristics and Optimization in Civil Engineering, Vol. 7, pp. 255-276, 2016.
[33] X. Cai, X. Gao and Y. Xue, “Improved bat algorithm with optimal forage strategy and random disturbance strategy”, International Journal of Bio-Inspired Computation, vol. 8, Issue 4, pp. 205-214,2016.
[34] B. Zhu, W. Zhu, Z. Liu, Q. Duan, and L. Cao, “A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization”, Computational Intelligence and Neuroscience, pp. 1-17, 2016.
[35] C. Yammani, S. Maheswarapu, and S. Matam, “A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models”, International Journal of Electrical Power & Energy Systems, vol. 79, pp. 120-131, 2016.
[36] M. Eusuff, K. Lansey and F. Pasha, “Shuffled frog-leaping algorithm : A Memetic Meta-Heuristic for Discrete Optimization”, Eng. Optim., vol. 38, Issue 2, pp. 129–154, 2005.