Open Access
RAIRO-Oper. Res.
Volume 57, Number 5, September-October 2023
Page(s) 2659 - 2685
Published online 16 October 2023
  • N. Adil and H. Lakhbab, A discrete bat algorithm for the multi-compartment vehicle routing problem, in 2020 IEEE 6th International Conference on Optimization and Applications (ICOA). IEEE (2020) 1–5. [Google Scholar]
  • M. Alinaghian and N. Shokouhi, Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search. Omega 76 (2018) 85–99. [CrossRef] [Google Scholar]
  • W.H. Bangyal, A. Hameed, J. Ahmad, K. Nisar, M.R. Haque, A.A.A. Ibrahim, J.J. Rodrigues, A. Khan, D.B. Rawat and R. Etengu, New modified controlled bat algorithm for numerical optimization problem. Comput. Mater. Continua 70 (2022) 2241–2259. [CrossRef] [Google Scholar]
  • A. Chakri, R. Khelif, M. Benouaret and X.S. Yang, New directional bat algorithm for continuous optimization problems. Expert Syst. App. 69 (2017) 159–175. [CrossRef] [Google Scholar]
  • A. Chakri, X.S. Yang, R. Khelif and M. Benouaret, Reliability-based design optimization using the directional bat algorithm. Neural Comput. App. 30 (2018) 2381–2402. [CrossRef] [Google Scholar]
  • L. Davis, Handbook of Genetic Algorithms. CumInCAD (1991). [Google Scholar]
  • J. Derrac, S. García, D. Molina and F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1 (2011) 3–18. [CrossRef] [Google Scholar]
  • R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE (1995) 39–43. [Google Scholar]
  • C. Gan, W. Cao, M. Wu and X. Chen, A new bat algorithm based on iterative local search and stochastic inertia weight. Expert Syst. App. 104 (2018) 202–212. [CrossRef] [Google Scholar]
  • A.H. Gandomi and X.S. Yang, Chaotic bat algorithm. J. Comput. Sci. 5 (2014) 224–232. [CrossRef] [MathSciNet] [Google Scholar]
  • S. García, A. Fernández, J. Luengo and F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180 (2010) 2044–2064. [CrossRef] [Google Scholar]
  • Z.W. Geem, J.H. Kim and G. Loganathan, A new heuristic optimization algorithm: harmony search. Simulation 76 (2001) 60–68. [Google Scholar]
  • F. Glover, Tabu search – part I. ORSA J. Comput. 1 (1989) 190–206. [CrossRef] [Google Scholar]
  • D. Gupta, J. Arora, U. Agrawal, A. Khanna and V.H.C. de Albuquerque, Optimized binary bat algorithm for classification of white blood cells. Measurement 143 (2019) 180–190. [CrossRef] [Google Scholar]
  • X.S. He, W.J. Ding and X.S. Yang, Bat algorithm based on simulated annealing and Gaussian perturbations. Neural Comput. App. 25 (2014) 459–468. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95 – International Conference on Neural Networks. Vol. 4. IEEE (1995) 1942–1948. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. [Google Scholar]
  • Y. Li, X. Li, J. Liu and X. Ruan, An improved bat algorithm based on Lévy flights and adjustment factors. Symmetry 11 (2019) 925. [CrossRef] [Google Scholar]
  • M. Melanie, An Introduction to Genetic Algorithms. MIT Press (1996). [Google Scholar]
  • X.B. Meng, X.Z. Gao, Y. Liu and H. Zhang, A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Syst. App. 42 (2015) 6350–6364. [CrossRef] [Google Scholar]
  • P. Musikapun and P. Pongcharoen, Solving multi-stage multi-machine multi-product scheduling problem using bat algorithm, in 2nd International Conference on Management and Artificial Intelligence, IPEDR. IACSIT Press, Singapore (2012) 35. [Google Scholar]
  • E. Osaba, X.S. Yang, F. Diaz, P. Lopez-Garcia and R. Carballedo, An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng. App. Artif. Intell. 48 (2016) 59–71. [CrossRef] [Google Scholar]
  • H.T. Rauf, S. Malik, U. Shoaib, M.N. Irfan and M.I. Lali, Adaptive inertia weight bat algorithm with Sugeno-Function fuzzy search. Appl. Soft Comput. 90 (2020) 106159. [CrossRef] [Google Scholar]
  • A. Rezaee Jordehi, Chaotic bat swarm optimisation (CBSO). Appl. Soft Comput. 26 (2015) 523–530. [CrossRef] [Google Scholar]
  • S. Ropke and D. Pisinger, An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40 (2006) 455–472. [CrossRef] [Google Scholar]
  • D.K. Sambariya and R. Prasad, Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm. Int. J. Electr. Power Energy Syst. 61 (2014) 229–238. [CrossRef] [Google Scholar]
  • S.C. Satapathy, N. Sri Madhava Raja, V. Rajinikanth, A.S. Ashour and N. Dey, Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput. App. 29 (2018) 1285–1307. [CrossRef] [Google Scholar]
  • Y. Shi and R. Eberhart, A modified particle swarm optimizer, in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). IEEE (1998) 69–73. [Google Scholar]
  • P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger and S. Tiwari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005 (2005). [Google Scholar]
  • T.K. Tharakeshwar, K.N. Seetharamu and B. Durga Prasad, Multi-objective optimization using bat algorithm for shell and tube heat exchangers. Appl. Thermal Eng. 110 (2017) 1029–1038. [CrossRef] [Google Scholar]
  • A.O. Topal and O. Altun, A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf. Sci. 354 (2016) 222–235. [CrossRef] [Google Scholar]
  • C. Wang, W. Song and P. Shen, A new bat algorithm based on a novel topology and its convergence. J. Comput. Sci. 66 (2023) 101931. [CrossRef] [Google Scholar]
  • X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Studies in Computational Intelligence, edited by JJ.R. González, D.A. Pelta, C. Cruz, G. Terrazas and N. Krasnogor. Springer (2010) 65–74. [Google Scholar]
  • X.S. Yang, Bat algorithm for multi-objective optimisation. Int. J. Bio-Inspired Comput. 3 (2011) 267. [CrossRef] [Google Scholar]
  • X.S. Yang and S. Deb, Cuckoo search via Lévy flights, in World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE (2009) 210–214. [Google Scholar]
  • G. Yildizdan and M.K. Baykan, A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst. App. 141 (2020) 112949. [CrossRef] [Google Scholar]
  • S. Yılmaz and E.U. Kisüçüksille, A new modification approach on bat algorithm for solving optimization problems. Appl. Soft Comput. 28 (2015) 259–275. [CrossRef] [Google Scholar]
  • M. Zhang, Z. Cui, Y. Chang, Y. Ren, X. Cai and H. Wang, Bat algorithm with individual local search, in Intelligence Science II. Springer International Publishing, Cham (2018) 442–451. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.