A comprehensive survey on the ant lion optimiser, variants and applications

References

• Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PloS one, 10(5), e0122827. https://doi.org/10.1371/journal.pone.0122827
   PubMed Web of Science ®Google Scholar
• Abduljabbar, D. A., Hashim, S. Z. M., & Sallehuddin, R. (2020). Nature-inspired optimization algorithms for community detection in complex networks: A review and future trends. Telecommunication Systems,74, 225–252. https://doi.org/10.1007/s11235-020-00733-2
   Web of Science ®Google Scholar
• Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8–22. https://doi.org/10.1016/j.swevo.2015.07.002
   Web of Science ®Google Scholar
• Abualigah, L. M. Q. (2019). Feature selection and enhanced krill herd algorithm for text document clustering. Springer.
   Google Scholar
• Abualigah, L., Shehab, M., Alshinwan, M., Mirjalili, S., & Abd Elaziz, M. (2020). Ant lion optimizer: a comprehensive survey of its variants and applications. Archives of Computational Methods in Engineering.28, 1397–1416, https://doi.org/10.1007/s11831-020-09420-6
   Web of Science ®Google Scholar
• Abualigah, L., Abd Elaziz, M., Hussien, A. G., Alsalibi, B., Jalali, S. M. J., & Gandomi, A. H. (2020). Lightning search algorithm: A comprehensive survey. Applied Intelligence (Dordrecht, Netherlands), 51, 2353–2376. https://doi.org/10.1007/s10489-020-01947-2
   PubMed Web of Science ®Google Scholar
• Abualigah, L., & Diabat, A. (2020). A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing, 24, 205–223 https://doi.org/10.1007/s10586-020-03075-5
   Web of Science ®Google Scholar
• Abualigah, L. (2020a). Group search optimizer: A nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Computing and Applications, 33, 2949–2972. https://doi.org/10.1007/s00521-020-05107-y
   Web of Science ®Google Scholar
• Abualigah, L. (2020b). Multi-verse optimizer algorithm: A comprehensive survey of its results, variants, and applications. Neural Computing and Applications, 32, 12381–12401. https://doi.org/10.1007/s00521-020-04839-1
   Web of Science ®Google Scholar
• Abul’Wafa, A. R. (2019). Ant‐lion optimizer‐based multi‐objective optimal simultaneous allocation of distributed generations and synchronous condensers in distribution networks. International Transactions on Electrical Energy Systems, 29(3), e2755. https://doi.org/10.1002/etep.2755
   Web of Science ®Google Scholar
• Agharazi, H., Kolacinski, R. M., Theeranaew, W., & Loparo, K. A. (2019). A swarm intelligence-based approach to anomaly detection of dynamic systems. Swarm and Evolutionary Computation, 44, 806–827. https://doi.org/10.1016/j.swevo.2018.09.003
   Web of Science ®Google Scholar
• Alazemi, F. Z., & Hatata, A. Y. (2019). Ant lion optimizer for optimum economic dispatch considering demand response as a visual power plant. Electric Power Components and Systems, 47(6–7), 629–643. https://doi.org/10.1080/15325008.2019.1602799
   Web of Science ®Google Scholar
• Ali, E. S., Elazim, S. A., & Abdelaziz, A. Y. (2016). Ant lion optimization algorithm for renewable distributed generations. Energy, 116 Part–1 , 445–458. https://doi.org/10.1016/j.energy.2016.09.104
   Google Scholar
• Ali, E. S., Elazim, S. A., & Abdelaziz, A. Y. (2018). Optimal allocation and sizing of renewable distributed generation using ant lion optimization algorithm. Electrical Engineering, 100(1), 99–109. https://doi.org/10.1007/s00202-016-0477-z
   Web of Science ®Google Scholar
• Amaireh, A. A., Al-Zoubi, A. S., & Dib, N. I. (2019). Sidelobe-level suppression for circular antenna array via new hybrid optimization algorithm based on antlion and grasshopper optimization algorithms. Progress In Electromagnetics Research, 93, 49–63. https://doi.org/10.2528/PIERC19040909
   Google Scholar
• Amroune, M., Musirin, I., Bouktir, T., & Othman, M. M. (2017). The amalgamation of SVR and ANFIS models with synchronized phasor measurements for on-line voltage stability assessment. Energies, 10(11), 1693. https://doi.org/10.3390/en10111693
   Web of Science ®Google Scholar
• Anter, A. M., & Zhang, Z. (2019, October). E-health Parkinson disease diagnosis in smart home based on hybrid intelligence optimization model. In International Conference on Advanced Intelligent Systems and Informatics (pp. 156–165). Springer, Cham.
   Google Scholar
• Arora, S., & Singh, S. (2017). An improved butterfly optimization algorithm with chaos. Journal of Intelligent & Fuzzy Systems, 32(1), 1079–1088. https://doi.org/10.3233/JIFS-16798
   Web of Science ®Google Scholar
• Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4
   Web of Science ®Google Scholar
• Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
   Web of Science ®Google Scholar
• Assiri, A. S., Hussien, A. G., & Amin, M. (2020). Ant Lion Optimization: Variants, hybrids, and applications. IEEE Access, 8, 77746–77764. https://doi.org/10.1109/ACCESS.2020.2990338
   Web of Science ®Google Scholar
• Azizi, M., Ghasemi, S. A. M., Ejlali, R. G., & Talatahari, S. (2019). Optimum design of fuzzy controller using hybrid ant lion optimizer and jaya algorithm. Artificial Intelligence Review, 53, 1553–1584. https://doi.org/10.1007/s10462-019-09713-8
   Web of Science ®Google Scholar
• Barma, P. S., Dutta, J., & Mukherjee, A. (2019). A 2-opt guided discrete antlion optimization algorithm for multi-depot vehicle routing problem. Decision Making: Applications in Management and Engineering, 2(2), 112–125. https://doi.org/10.31181/dmame1902089b
   Google Scholar
• Baş, E., & Ülker, E. (2020). A binary social spider algorithm for continuous optimization task. Soft Computing, 24, 12953–12979. https://doi.org/10.1007/s00500-020-04718-w
   Web of Science ®Google Scholar
• Berger-Tal, O., Nathan, J., Meron, E., & Saltz, D. (2014). The exploration-exploitation dilemma: A multidisciplinary framework. PloS one, 9(4), e95693. https://doi.org/10.1371/journal.pone.0095693
   PubMed Web of Science ®Google Scholar
• BoussaïD, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117. https://doi.org/10.1016/j.ins.2013.02.041
   Web of Science ®Google Scholar
• Bozorg-Haddad, O. (Ed.). (2018). Advanced optimization by nature-inspired algorithms. Springer.
   Google Scholar
• Brezočnik, L., Fister, I., & Podgorelec, V. (2018). Swarm intelligence algorithms for feature selection: A review. Applied Sciences, 8(9), 1521. https://doi.org/10.3390/app8091521
   Google Scholar
• Buch, H., & Trivedi, I. N. (2019). On the efficiency of metaheuristics for solving the optimal power flow. Neural Computing and Applications, 31(9), 5609–5627. https://doi.org/10.1007/s00521-018-3382-8
   Web of Science ®Google Scholar
• Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Qu, R. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724. https://doi.org/10.1057/jors.2013.71
   Web of Science ®Google Scholar
• Cao, W. D., Yan, C. P., Wu, D. J., & Tuo, J. B. (2017). A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing. The International Journal of Advanced Manufacturing Technology, 93(9–12), 4099–4110. https://doi.org/10.1007/s00170-017-0823-y
   Web of Science ®Google Scholar
• Chen, J., Cai, H., & Wang, W. (2018). A new metaheuristic algorithm: Car tracking optimization algorithm. Soft Computing, 22(12), 3857–3878. https://doi.org/10.1007/s00500-017-2845-7
   Web of Science ®Google Scholar
• Chen, C., & Yu, L. (2019). A hybrid ant lion optimizer with improved nelder–mead algorithm for structural damage detection by improving weighted trace lasso regularization. Advances in Structural Engineering, 23(3), 468–484. https://doi.org/10.1177/1369433219872434
   Web of Science ®Google Scholar
• Chopra, N., & Mehta, S. (2015, December). Multi-objective optimum generation scheduling using ant lion optimization. In 2015 annual IEEE India conference (INDICON) (pp. 1–6). IEEE. New Delhi India.
   Google Scholar
• Christaline, J. A., Ramesh, R., & Vaishali, D. (2016). Bio-inspired computational algorithms for improved image steganalysis. Indian Journal of Science and Technology, 9(10), 1e10. https://doi.org/10.17485/ijst/2016/v9i10/88995
   Google Scholar
• Christaline, A., Ramesh, R., Gomathy, C., & Vaishali, D. (2017). Bio inspired optimization for universal spatial image steganalysis. Journal of Computational Science, 21, 182–188. https://doi.org/10.1016/j.jocs.2017.06.014
   Web of Science ®Google Scholar
• Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854–858). Springer, Berlin Heidelberg.
   Google Scholar
• Coello, C. A. C., Brambila, S. G., Gamboa, J. F., Tapia, M. G. C., & Gómez, R. H. (2019). Evolutionary multiobjective optimization: Open research areas and some challenges lying ahead. Complex & Intelligent Systems, 6 (2) , 221–236. https://doi.org/10.1007/s40747-019-0113-4
   Web of Science ®Google Scholar
• Dalwinder, S., Birmohan, S., & Manpreet, K. (2019). Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer. Biocybernetics and Biomedical Engineering. 40(1), 337–351. https://doi.org/10.1016/j.bbe.2019.12.004
   Web of Science ®Google Scholar
• Darwish, A. (2018). Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2), 231–246. https://doi.org/10.1016/j.fcij.2018.06.001
   Google Scholar
• Davis, L. 1991. Handbook of Genetic Algorithms Search PubMed. Van Nostrand Reinhold.
   Google Scholar
• Dhal, K. G., Das, A., Ray, S., Gálvez, J., & Das, S. (2019). Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation. Archives of Computational Methods in Engineering, 27 (3) , 855–888. https://doi.org/10.1007/s11831-019-09334-y
   Web of Science ®Google Scholar
• Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014
   Web of Science ®Google Scholar
• Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50. https://doi.org/10.1016/j.knosys.2018.06.001
   Web of Science ®Google Scholar
• Ding, S. (2019). A novel discrete grey multivariable model and its application in forecasting the output value of China’s high-tech industries. Computers & Industrial Engineering, 127, 749–760. https://doi.org/10.1016/j.cie.2018.11.016
   Web of Science ®Google Scholar
• Dinkar, S. K., & Deep, K. (2017). Opposition based Laplacian ant lion optimizer. Journal of Computational Science, 23, 71–90. https://doi.org/10.1016/j.jocs.2017.10.007
   Web of Science ®Google Scholar
• Dinkar, S. K., & Deep, K. (2019a). Accelerated opposition-based antlion optimizer with application to order reduction of linear time-invariant systems. Arabian Journal for Science and Engineering, 44(3), 2213–2241. https://doi.org/10.1007/s13369-018-3370-4
   Web of Science ®Google Scholar
• Dinkar, S. K., & Deep, K. (2019b). Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem. Neural Computing and Applications,32 (11) , 6967–6995. https://doi.org/10.1007/s00521-019-04095-y
   Web of Science ®Google Scholar
• Du, P., Wang, J., Guo, Z., & Yang, W. (2017). Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Conversion and Management, 150, 90–107. https://doi.org/10.1016/j.enconman.2017.07.065
   Web of Science ®Google Scholar
• Dubey, R., Joshi, D., & Bansal, R. C. (2016). Optimization of solar photovoltaic plant and economic analysis. Electric Power Components and Systems, 44(18), 2025–2035. https://doi.org/10.1080/15325008.2016.1209706
   Web of Science ®Google Scholar
• Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942–1948). Citeseer. Perth: WA: Australia
   Google Scholar
• Elaziz, M. A., Moemen, Y. S., Hassanien, A. E., & Xiong, S. (2018). Quantitative structure-activity relationship model for hcvns5b inhibitors based on an antlion optimizer-adaptive neuro-fuzzy inference system. Scientific Reports, 8(1), 1506. https://doi.org/10.1038/s41598-017-19122-y
   PubMedGoogle Scholar
• Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary ant lion approaches for feature selection. Neurocomputing, 213, 54–65. https://doi.org/10.1016/j.neucom.2016.03.101
   Web of Science ®Google Scholar
• Emary, E., & Zawbaa, H. M. (2019). Feature selection via Lèvy Antlion optimization. Pattern Analysis and Applications, 22(3), 857–876. https://doi.org/10.1007/s10044-018-0695-2
   Web of Science ®Google Scholar
• Evangelin, L. N., & Fred, A. L. (2019). Reduced optimal feature based biometric authentication using MALO-MKSVM techniques. Multimedia Tools and Applications, 78(22), 31077–31100. https://doi.org/10.1007/s11042-019-07918-1
   Web of Science ®Google Scholar
• Farughi, H., Mostafayi, S., & Arkat, J. (2019). Healthcare districting optimization using gray wolf optimizer and ant lion optimizer algorithms (case study: South Khorasan Healthcare System in Iran). Journal of Optimization in Industrial Engineering, 12(1), 119–131. https://doi.org/10.22094/JOIE.2018.766.1489
   Google Scholar
• Fathy, A., & Abdelaziz, A. Y. (2018). Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms. International Journal of Energy and Environmental Engineering, 9(3), 257–271. https://doi.org/10.1007/s40095-018-0266-8
   Google Scholar
• Fathy, A., & Kassem, A. M. (2019). Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine. ISA transactions, 87, 282–296. https://doi.org/10.1016/j.isatra.2018.11.035
   PubMed Web of Science ®Google Scholar
• Filali, W., Garoudja, E., Oussalah, S., Mekheldi, M., Sengouga, N., & Henini, M. (2019). A novel parameter identification approach for C–V–T characteristics of multi-quantum wells schottky diode using ant lion optimizer. Russian Microelectronics, 48(6), 428–434. https://doi.org/10.1134/S1063739719660028
   Google Scholar
• Fister, I., Strnad, D., & Yang, X. S. (2015). Adaptation and hybridization in nature-inspired algorithms. In Iztok Fister Jr. Ed. Adaptation and hybridization in computational intelligence (pp. 3–50). Springer.
   Google Scholar
• Formato, R. A. (2008). Central force optimization: A new nature inspired computational framework for multidimensional search and optimization. In Natalio Krasnogor, Giuseppe Nicosia, Mario Pavone, David Pelta, Eds. Nature Inspired Cooperative Strategies for Optimization (NICSO 2007) (pp. 221–238). Springer.
   Google Scholar
• Gajula, V., & Rajathy, R. (2019). An agile optimization algorithm for vitality management along with fusion of sustainable renewable resources in microgrid. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(13), 1580–1598. https://doi.org/10.1080/15567036.2019.1604869
   Web of Science ®Google Scholar
• Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010
   Web of Science ®Google Scholar
• Gandomi, A. H., & Kashani, A. R. (2017). Construction cost minimization of shallow foundation using recent swarm intelligence techniques. IEEE Transactions on Industrial Informatics, 14(3), 1099–1106. https://doi.org/10.1109/TII.2017.2776132
   Web of Science ®Google Scholar
• Garaigordobil, A., Ansola, R., Santamaría, J., & de Bustos, I. F. (2018). A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing. Structural and Multidisciplinary Optimization, 58(5), 2003–2017. https://doi.org/10.1007/s00158-018-2010-7
   Web of Science ®Google Scholar
• Gautam, R., Kaur, P., & Sharma, M. (2019). A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings. Progress in Artificial Intelligence, 8 (4) , 401–424. https://doi.org/10.1007/s13748-019-00191-1
   Web of Science ®Google Scholar
• Ghafil, H. N., & Jármai, K. (2020). Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications. Applied Soft Computing, 93, 106392. https://doi.org/10.1016/j.asoc.2020.106392
   Web of Science ®Google Scholar
• Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2019). A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 53 (3) , 2265–2312. https://doi.org/10.1007/s10462-019-09733-4
   Web of Science ®Google Scholar
• Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: whale optimization algorithm and its applications. Swarm and Evolutionary Computation, 48, 1–24. https://doi.org/10.1016/j.swevo.2019.03.004
   Web of Science ®Google Scholar
• Ghimatgar, H., Kazemi, K., Helfroush, M. S., & Aarabi, A. (2018). An improved feature selection algorithm based on graph clustering and ant colony optimization. Knowledge-Based Systems, 159, 270–285. https://doi.org/10.1016/j.knosys.2018.06.025
   Web of Science ®Google Scholar
• Guo, J., Yan, D., Cao, H., & Jiang, Z. (2016). The point to point trajectory planning based on the ant lion optimiser. International Journal of Automation and Control, 10(2), 155–166. https://doi.org/10.1504/IJAAC.2016.076457
   Google Scholar
• Guo, S., Zhao, H., & Zhao, H. (2017). A new hybrid wind power forecaster using the Beveridge-nelson decomposition method and a relevance vector machine optimized by the ant lion optimizer. Energies, 10(7), 922. https://doi.org/10.3390/en10070922
   Web of Science ®Google Scholar
• GUO, W. Y., LIU, K. X., Zhang, X., & Zhang, J. J. (2018). Antlion optimization algorithm based on quadratic interpolation. DEStech Transactions on Computer Science and Engineering 411–417 . cnai. https://doi.org/10.12783/dtcse/cnai2018/24190
   Google Scholar
• Hadidian-Moghaddam, M. J., Arabi-Nowdeh, S., Bigdeli, M., & Azizian, D. (2018). A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique. Ain Shams Engineering Journal, 9(4), 2101–2109. https://doi.org/10.1016/j.asej.2017.03.001
   Web of Science ®Google Scholar
• Hamouda, E., El-Metwally, S., & Tarek, M. (2018). Ant lion optimization algorithm for kidney exchanges. PloS one, 13(5), e0196707. https://doi.org/10.1371/journal.pone.0196707
   PubMed Web of Science ®Google Scholar
• Hancer, E., & Karaboga, D. (2017). A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm and Evolutionary Computation, 32, 49–67. https://doi.org/10.1016/j.swevo.2016.06.004
   Web of Science ®Google Scholar
• Hancer, E., Xue, B., & Zhang, M. (2018). Differential evolution for filter feature selection based on information theory and feature ranking. Knowledge-Based Systems, 140, 103–119. https://doi.org/10.1016/j.knosys.2017.10.028
   Web of Science ®Google Scholar
• Hansen, N., & Kern, S. (2004, September). Evaluating the CMA evolution strategy on multimodal test functions. In International Conference on Parallel Problem Solving from Nature (pp. 282–291). Springer, Berlin, Heidelberg.
   Google Scholar
• Hatata, A. Y., & Hafez, A. A. (2019). Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation. International Transactions on Electrical Energy Systems, 29(4), e2803. https://doi.org/10.1002/etep.2803
   Web of Science ®Google Scholar
• He, Y., & Wang, X. (2018). Group theory-based optimization algorithm for solving knapsack problems. Knowledge-Based Systems, 219, 104445. https://doi.org/10.1016/j.knosys.2018.07.045
   Google Scholar
• Heidari, A. A., Faris, H., Mirjalili, S., Aljarah, I., & Mafarja, M. (2020). Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks. In Seyedali Mirjalili, Jin Song Dong, Andrew Lewis, Eds. Nature-inspired optimizers (pp. 23–46). Springer.
   Google Scholar
• Hellwig, M., & Beyer, H. G. (2019). Benchmarking evolutionary algorithms for single objective real-valued constrained optimization–a critical review. Swarm and Evolutionary Computation, 44, 927–944. https://doi.org/10.1016/j.swevo.2018.10.002
   Web of Science ®Google Scholar
• Hema, P., Rao, J. M., & Reddy, C. E. (2020). Machining of bio-implant materials using WEDM and optimization of process parameters. In M. S. Shunmugam, M. Kanthababu, Eds. Advances in unconventional machining and composites (pp. 397–411). Springer.
   Google Scholar
• Herbadji, O., Slimani, L., & Bouktir, T. (2019). Optimal power flow with four conflicting objective functions using multi-objective ant lion algorithm: A case study of the Algerian electrical network. Iranian Journal of Electrical and Electronic Engineering, 15(1), 94–113. http://ijeee.iust.ac.ir/article-1-1302-en.html
   Google Scholar
• Hu, H., Li, Y., Bai, Y., Zhang, J., & Liu, M. (2019). The improved antlion optimizer and artificial neural network for Chinese influenza prediction. Complexity, 2019, 1–12/ 1480392. https://doi.org/10.1155/2019/1480392
   Web of Science ®Google Scholar
• Hu, X., Yao, L., Zhang, Y., Meng, Z., & Sun, Y. (2019). Optimizing structural parameters of carbon fiber braiding carriers based on antlion optimization algorithm. Journal of Industrial Textiles, 50(4), 460–482. https://doi.org/10.1177/1528083719831085
   Web of Science ®Google Scholar
• Hussain, K., Salleh, M. N. M., Cheng, S., & Shi, Y. (2019). On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications, 31(11), 7665–7683. https://doi.org/10.1007/s00521-018-3592-0
   Web of Science ®Google Scholar
• Hussien, A. G., Oliva, D., Houssein, E. H., Juan, A. A., & Yu, X. (2020). Binary whale optimization algorithm for dimensionality reduction. Mathematics, 8(10), 1821. https://doi.org/10.3390/math8101821
   Web of Science ®Google Scholar
• Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: Theory, recent advances, and applications. IEEE Access, 8, 173548–173565. https://doi.org/10.1109/ACCESS.2020.3024108
   Google Scholar
• Ibrahim, I. A., Hossain, J., & Duck, B. C. (2019). An optimized offline random forests-based model for ultra-short-term prediction of PV characteristics. IEEE Transactions on Industrial Informatics, 16(1), 202–214. https://doi.org/10.1109/TII.2019.2916566
   Web of Science ®Google Scholar
• Jin, Y. (2011). Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61–70. https://doi.org/10.1016/j.swevo.2011.05.001
   Web of Science ®Google Scholar
• Jin, C., Ye, Z., Yan, L., Cao, Y., Zhang, A., Ma, L., … Hu, J. (2019, September). Image segmentation using fuzzy c-means optimized by ant lion optimization. In 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 1, pp. 388–393). IEEE. Metz, France.
   Google Scholar
• Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
   Web of Science ®Google Scholar
• Kaabeche, A., & Bakelli, Y. (2019). Renewable hybrid system size optimization considering various electrochemical energy storage technologies. Energy Conversion and Management, 193, 162–175. https://doi.org/10.1016/j.enconman.2019.04.064
   Web of Science ®Google Scholar
• Kaidi, W., Khishe, M., & Mohammadi, M. (2022). Dynamic levy flight chimp optimization. Knowledge-Based Systems, 235, 107625. https://doi.org/10.1016/j.knosys.2021.107625
   Web of Science ®Google Scholar
• Kaleem, A. M., & Kokate, R. D. (2019). Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM. Journal of Ambient Intelligence and Humanized Computing, 12, 3689–3703. https://doi.org/10.1007/s12652-019-01648-w
   Web of Science ®Google Scholar
• Kamboj, V. K., Bhadoria, A., & Bath, S. K. (2017). Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer. Neural Computing and Applications, 28(8), 2181–2192. https://doi.org/10.1007/s00521-015-2148-9
   Web of Science ®Google Scholar
• Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697. https://doi.org/10.1016/j.asoc.2007.05.007
   Web of Science ®Google Scholar
• Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: Charged system search. Acta Mechanica, 213(3), 267–289. https://doi.org/10.1007/s00707-009-0270-4
   Web of Science ®Google Scholar
• Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray optimization. Computers & Structures, 112, 283–294. https://doi.org/10.1016/j.compstruc.2012.09.003
   Web of Science ®Google Scholar
• Kaveh, A., & Vazirinia, Y. (2019). Smart-home electrical energy scheduling system using multi-objective ant lion optimizer and evidential reasoning. Scientia Iranica.
   Google Scholar
• Kaveh, M., Chayjan, R. A., Taghinezhad, E., Gilandeh, Y. A., Younesi, A., & Sharabiani, V. R. (2019). Modeling of thermodynamic properties of carrot product using ALO, GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer. Engineering with Computers, 35(3), 1045–1058. https://doi.org/10.1007/s00366-018-0650-2
   Web of Science ®Google Scholar
• Ke, Q., Zhang, J., Wei, W., Połap, D., Woźniak, M., Kośmider, L., & Damaševĭcius, R. (2019). A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Systems with Applications, 126, 218–232. https://doi.org/10.1016/j.eswa.2019.01.060
   Web of Science ®Google Scholar
• Khishe, M., & Mosavi, M. R. (2020). Chimp optimization algorithm. Expert Systems with Applications, 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338
   Web of Science ®Google Scholar
• Khisheh, M., Aghababaei, M., Saffari, A., & Goldani, A. (2016). AUV’s sensor selection by using ant-lion optimization algorithm and neural networks. Iranian Journal of Marine Science and Technology, 20(77), 59–69.
   Google Scholar
• Kilic, H., Yuzgec, U., & Karakuzu, C. (2019). Improved antlion optimizer algorithm and its performance on neuro fuzzy inference system. Neural Network World, 29(4), 235–254. https://doi.org/10.14311/NNW.2019.29.016
   Web of Science ®Google Scholar
• Kilic, H., Yuzgec, U., & Karakuzu, C. (2020). A novel improved antlion optimizer algorithm and its comparative performance. Neural Computing and Applications, 32(8), 3803–3824. https://doi.org/10.1007/s00521-018-3871-9
   Web of Science ®Google Scholar
• Kılıç, H., & Yüzgeç, U. (2019a). Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling. Computers & Industrial Engineering, 132, 166–186. https://doi.org/10.1016/j.cie.2019.04.029
   Web of Science ®Google Scholar
• Kılıç, H., & Yüzgeç, U. (2019b). Tournament selection based antlion optimization algorithm for solving quadratic assignment problem. Engineering Science and Technology, an International Journal, 22(2), 673–691. https://doi.org/10.1016/j.jestch.2018.11.013
   Google Scholar
• Kose, U. (2018). An ant-lion optimizer-trained artificial neural network system for chaotic electroencephalogram (EEG) prediction. Applied Sciences, 8(9), 1613. https://doi.org/10.3390/app8091613
   Google Scholar
• Li, R., & Jin, Y. (2018). A wind speed interval prediction system based on multi-objective optimization for machine learning method. Applied Energy, 228, 2207–2220. https://doi.org/10.1016/j.apenergy.2018.07.032
   Web of Science ®Google Scholar
• Li, Y., Feng, B., Li, G., Qi, J., Zhao, D., & Mu, Y. (2018). Optimal distributed generation planning in active distribution networks considering integration of energy storage. Applied Energy, 210, 1073–1081. https://doi.org/10.1016/j.apenergy.2017.08.008
   Web of Science ®Google Scholar
• Li, L. L., Zhang, X. B., Tseng, M. L., & Zhou, Y. T. (2019). Optimal scale Gaussian process regression model in insulated gate bipolar transistor remaining life prediction. Applied Soft Computing, 78, 261–273. https://doi.org/10.1016/j.asoc.2019.02.035
   Web of Science ®Google Scholar
• Li, Z., Cao, Y., Dai, L. V., Yang, X., & Nguyen, T. T. (2019). Finding solutions for optimal reactive power dispatch problem by a novel improved antlion optimization algorithm. Energies, 12(15), 2968. https://doi.org/10.3390/en12152968
   Web of Science ®Google Scholar
• Liu, W., Moayedi, H., Nguyen, H., Lyu, Z., & Bui, D. T. (2019). Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil. Engineering with Computers, 37 (2) , 1537–1547. https://doi.org/10.1007/s00366-019-00897-9
   Web of Science ®Google Scholar
• Lynn, N., Ali, M. Z., & Suganthan, P. N. (2018). Population topologies for particle swarm optimization and differential evolution. Swarm and Evolutionary Computation, 39, 24–35. https://doi.org/10.1016/j.swevo.2017.11.002
   Web of Science ®Google Scholar
• Mafarja, M., Eleyan, D., Abdullah, S., & Mirjalili, S. (2017, July). S-shaped vs. V-shaped transfer functions for ant lion optimization algorithm in feature selection problem. In Proceedings of the international conference on future networks and distributed systems (p. 21). ACM. Cambridge, United Kingdom
   Google Scholar
• Mafarja, M. M., & Mirjalili, S. (2019). Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Computing, 23(15), 6249–6265. https://doi.org/10.1007/s00500-018-3282-y
   Web of Science ®Google Scholar
• Mageshkumar, C., Karthik, S., & Arunachalam, V. P. (2019). Hybrid metaheuristic algorithm for improving the efficiency of data clustering. Cluster Computing, 22(1), 435–442. https://doi.org/10.1007/s10586-018-2242-8
   Google Scholar
• Mahanta, G. B., Rout, A. V. L. D. B., & Biswal, B. B. (2019). An improved multi-objective antlion optimization algorithm for the optimal design of the robotic gripper. Journal of Experimental & Theoretical Artificial Intelligence, 32(2), 309–338 https://doi.org/10.1080/0952813X.2019.1647565
   Web of Science ®Google Scholar
• Mahendran, K., & Prabha, S. U. (2019). Optimal control strategies for a hybrid renewable energy system: An ALANN/RNN technique. Soft Computing, 23 (24) , 13459–13475. https://doi.org/10.1007/s00500-019-03885-9
   Web of Science ®Google Scholar
• Majhi, S. K., & Biswal, S. (2018). Optimal cluster analysis using hybrid K-means and ant lion optimizer. Karbala International Journal of Modern Science, 4(4), 347–360. https://doi.org/10.1016/j.kijoms.2018.09.001
   Google Scholar
• Manoharan, H., Srikrishna, S., Sivarajan, G., & Manoharan, A. (2018). Economical placement of PMUs considering observability and voltage stability using binary coded ant lion optimization. International Transactions on Electrical Energy Systems, 28(9), e2591. https://doi.org/10.1002/etep.2591
   Web of Science ®Google Scholar
• Manuel, R., & Emayavaramban, G. (2019). PALONN: parallel ant lion optimizer and artificial neural network for power flow control of the micro grid-connected system. IETE Journal of Research, 68(2), 1225–1242 https://doi.org/10.1080/03772063.2019.1644208
   Web of Science ®Google Scholar
• Mavrovouniotis, M., Li, C., & Yang, S. (2017). A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, 1–17. https://doi.org/10.1016/j.swevo.2016.12.005
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
   Web of Science ®Google Scholar
• Mirjalili, S. (2015a). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006
   Web of Science ®Google Scholar
• Mirjalili, S. (2015b). The ant lion optimizer. Advances in Engineering Software, 83, 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
   Web of Science ®Google Scholar
• Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1
   Web of Science ®Google Scholar
• Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95. https://doi.org/10.1007/s10489-016-0825-8
   Web of Science ®Google Scholar
• Moayedi, H., Tien Bui, D., Anastasios, D., & Kalantar, B. (2019). Spotted hyena optimizer and ant lion optimization in predicting the shear strength of soil. Applied Sciences, 9(22), 4738. https://doi.org/10.3390/app9224738
   Google Scholar
• Mosbah, M., Arif, S., Mohammedi, R. D., & Zine, R. (2018, October). Optimal Algerian distribution network reconfiguration using antlion algorithm for active power losses. In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) (pp. 1–6). IEEE. Tebessa, Algeria.
   Google Scholar
• Mostafa, A., Houseni, M., Allam, N., Hassanien, A. E., Hefny, H., & Tsai, P. W. (2016, November). Antlion optimization based segmentation for MRI liver images. In International Conference on Genetic and Evolutionary Computing (pp. 265–272). Springer, Cham.
   Google Scholar
• Mouassa, S., Bouktir, T., & Salhi, A. (2017). Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Engineering Science and Technology, an International Journal, 20(3), 885–895. https://doi.org/10.1016/j.jestch.2017.03.006
   Google Scholar
• Mousavifard, R., Abolghasemzadeh, M., Razmjooy, N., & Alizadeh, Y. (2019). Optimal design of functionally graded steels using multi-objective ant lion optimizer. The 27th Annual International Conference of Iranian Society of Mechanical Engineers-ISME2019, Tehran, Iran.
   Google Scholar
• Newman, D., & Newman, D. (2007). UCI machine learning repository. International Journal of Urology: Official Journal of the Japanese Urological Association, 14(9), 862–864. https://doi.org/10.1111/j.1442-2042.2007.01827.x
   PubMedGoogle Scholar
• Oliva, D., Hinojosa, S., Elaziz, M. A., & Ortega-Sánchez, N. (2018). Context based image segmentation using antlion optimization and sine cosine algorithm. Multimedia Tools and Applications, 77(19), 25761–25797. https://doi.org/10.1007/s11042-018-5815-x
   Web of Science ®Google Scholar
• Panwar, L. K., Reddy, S., Verma, A., Panigrahi, B. K., & Kumar, R. (2018). Binary grey wolf optimizer for large scale unit commitment problem. Swarm and Evolutionary Computation, 38, 251–266. https://doi.org/10.1016/j.swevo.2017.08.002
   Web of Science ®Google Scholar
• Parvathi, P., & Rajeswari, R. (2016, October). A hybrid FCM-ALO based technique for image segmentation. In 2016 IEEE international conference on advances in computer applications (ICACA) (pp. 342–345). IEEE. Coimbatore, India.
   Google Scholar
• Pathak, V. K., & Srivastava, A. K. (2020). A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems. Engineering with Computers, 38, 1731–1758. https://doi.org/10.1007/s00366-020-01127-3
   Web of Science ®Google Scholar
• Pati, P. R., & Satpathy, M. P. (2019). Investigation on red brick dust filled epoxy composites using ant lion optimization approach. Polymer Composites, 40(10), 3877–3885. https://doi.org/10.1002/pc.25246
   Web of Science ®Google Scholar
• Petrovic, A., Delibasic, B., Filipovic, J., Petrovic, A., & Lomovic, M. (2018). Thermoeconomic and environmental optimization of geothermal water desalination plant with ejector refrigeration system. Energy Conversion and Management, 178, 65–77. https://doi.org/10.1016/j.enconman.2018.10.035
   Web of Science ®Google Scholar
• Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z., & Babić, B. (2016). The ant lion optimization algorithm for integrated process planning and scheduling. In Cristian Vasile Doicin, Nicolae Ionescu, Tom Savu, Eduard Niţu, Eds. Applied mechanics and materials (Vol. 834, pp. 187–192)., Trans Tech Publications Ltd.
   Google Scholar
• Pradhan, R., Majhi, S. K., & Pati, B. B. (2018). Design of PID controller for automatic voltage regulator system using ant lion optimizer. World Journal of Engineering, 15(3), 373–387. https://doi.org/10.1108/WJE-05-2017-0102
   Google Scholar
• Preetha, P. S., & Kusagur, A. (2020). Implementation of ant-lion optimization algorithm in energy management problem and comparison. In Suresh Chandra Satapathy, K. Srujan Raju, K. Shyamala, D. Rama Krishna, Margarita N. Favorskaya, Eds. Advances in decision sciences, image processing, security and computer vision (pp. 462–469). Springer.
   Google Scholar
• Premkumar, K., Manikandan, B. V., & Kumar, C. A. (2017). Antlion algorithm optimized fuzzy PID supervised on-line recurrent fuzzy neural network based controller for brushless DC motor. Electric Power Components and Systems, 45(20), 2304–2317. https://doi.org/10.1080/15325008.2017.1402395
   Web of Science ®Google Scholar
• Qin, J. (2009, November). A new optimization algorithm and its application—Key cutting algorithm. In 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009) (pp. 1537–1541). IEEE. Nanjing, China
   Google Scholar
• Qu, B. Y., Zhu, Y. S., Jiao, Y. C., Wu, M. Y., Suganthan, P. N., & Liang, J. J. (2018). A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm and Evolutionary Computation, 38, 1–11. https://doi.org/10.1016/j.swevo.2017.06.002
   Web of Science ®Google Scholar
• Radha, J., Subramanian, S., Ganesan, S., & Abirami, M. (2016). Best complex power settings using ant lion optimizer for optimal power flow problem. IUP Journal of Electrical & Electronics Engineering, 9(3), 7–31. https://ssrn.com/abstract=2961130
   Google Scholar
• Rajan, A., Jeevan, K., & Malakar, T. (2017). Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem. Applied Soft Computing, 55, 352–370. https://doi.org/10.1016/j.asoc.2017.02.010
   Web of Science ®Google Scholar
• Rani, R. R., & Ramyachitra, D. (2020). Antlion optimization algorithm for pairwise structural alignment with bi-objective functions. Neural Computing and Applications, 32, 7079–7096. https://doi.org/10.1007/s00521-019-04176-y
   Web of Science ®Google Scholar
• Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
   Web of Science ®Google Scholar
• Rayyam, M., & Zazi, M. (2019). A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer. Transactions of the Institute of Measurement and Control, 42(8), 1537–1546. https://doi.org/10.1177/0142331219892142
   Web of Science ®Google Scholar
• Reddy, P., Reddy, V. V., & Manohar, T. G. (2018). Ant lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. Journal of Electrical Systems and Information Technology, 5(3), 663–680. https://doi.org/10.1016/j.jesit.2017.06.001
   Google Scholar
• Reddy, M. P. K., & Babu, M. R. (2019). A hybrid cluster head selection model for Internet of Things.&nbsp. Cluster Computing, &nbsp, 22(6), 13095–13107. https://doi.org/10.1007/s10586-017-1261-1
   Google Scholar
• Ren, B., & Zhong, W. (2011). Multi-objective optimization using chaos based PSO. Information. Technology Journal,&nbsp, 10(10), 1908–1916. https://scialert.net/abstract/?doi=itj.2011.1908.1916
   Google Scholar
• Rizk-Allah, R. M. (2018). Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems.&nbsp. Journal of Computational Design and Engineering,&nbsp, 5(2), 249–273. https://doi.org/10.1016/j.jcde.2017.08.002
   Web of Science ®Google Scholar
• Rout, A., Mahanta, G. B., Bbvl, D., & Biswal, B. B. (2020). Kinematic and dynamic optimal trajectory planning of industrial robot using improved multi-objective ant lion optimizer. Journal of the Institution of Engineers (India): Series, C(101), 559–569. https://doi.org/10.1007/s40032-020-00557-8
   Google Scholar
• Roy, K., Mandal, K. K., & Mandal, A. C. (2019). Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system. Energy, 167, 402–416. https://doi.org/10.1016/j.energy.2018.10.153
   Web of Science ®Google Scholar
• Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026
   Web of Science ®Google Scholar
• Saxena, P., & Kothari, A. (2016). Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEU-International Journal of Electronics and Communications, 70(9), 1339–1349. https://doi.org/10.1016/j.aeue.2016.07.008
   Web of Science ®Google Scholar
• Scharf, I., & Ovadia, O. (2006). Factors influencing site abandonment and site selection in a sit-and-wait predator: A review of pit-building antlion larvae. Journal of Insect Behavior, 19(2), 197–218. https://doi.org/10.1007/s10905-006-9017-4
   Web of Science ®Google Scholar
• Scharf, I., Subach, A., & Ovadia, O. (2008). Foraging behaviour and habitat selection in pit-building antlion larvae in constant light or dark conditions. Animal Behaviour, 76(6), 2049–2057. https://doi.org/10.1016/j.anbehav.2008.08.023
   Google Scholar
• Sekhar, V., & Ravi, K. (2019). Low-voltage ride-through capability enhancement of wind energy conversion system using an ant-lion recurrent neural network controller. Measurement and Control, 52(7–8), 1048–1062. https://doi.org/10.1177/0020294019858102
   Web of Science ®Google Scholar
• Šenkeřík, R., Zelinka, I., Pluhacek, M., Viktorin, A., Janostik, J., & Oplatkova, Z. K. (2018). Randomization and complex networks for meta-heuristic algorithms. In Obdulia Pichardo-Lagunas, and Sabino Miranda-Jiménez, Eds. Evolutionary algorithms, swarm dynamics and complex networks (pp. 177–194). Springer.
   Google Scholar
• Shahinzadeh, H., Moradi, J., Gharehpetian, G. B., Fathi, S. H., & Abedi, M. (2018, November). Green power island, a blue battery concept for energy management of high penetration of renewable energy sources with techno-economic and environmental considerations. In 2018 Smart Grid Conference (SGC) (pp. 1–9). IEEE. Sanandaj, Iran.
   Google Scholar
• Shanmugam, C., & Sekaran, E. C. (2019). IRT image segmentation and enhancement using FCM-MALO approach. Infrared Physics & Technology, 97, 187–196. https://doi.org/10.1016/j.infrared.2018.12.032
   Web of Science ®Google Scholar
• Sharma, R., & Saha, A. (2019). Ant Lion optimizer for state based object oriented testing. Journal of Information and Optimization Sciences, 40(2), 219–232. https://doi.org/10.1080/02522667.2019.1578085
   Web of Science ®Google Scholar
• Shayanfar, H., & Gharehchopogh, F. S. (2018). Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 71, 728–746. https://doi.org/10.1016/j.asoc.2018.07.033
   Web of Science ®Google Scholar
• Siarry, P., Idoumghar, L., & Lepagnot, J. (2016). Swarm intelligence based optimization. Springer International Publishing AG.
   Google Scholar
• Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
   Web of Science ®Google Scholar
• Singh, P. R., Elaziz, M. A., & Xiong, S. (2018). Modified spider monkey optimization based on Nelder–Mead method for global optimization. Expert Systems with Applications, 110, 264–289. https://doi.org/10.1016/j.eswa.2018.05.040
   Web of Science ®Google Scholar
• Singh, D., & Singh, B. (2019). Hybridization of feature selection and feature weighting for high dimensional data. Applied Intelligence, 49(4), 1580–1596. https://doi.org/10.1007/s10489-018-1348-2
   Web of Science ®Google Scholar
• Singh, R. K., Gangwar, S., Singh, D. K., & Pathak, V. K. (2019). A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(6), 270. https://doi.org/10.1007/s40430-019-1778-8
   Web of Science ®Google Scholar
• Sita, H., Umapathi Reddy, P., & Kiranmayi, R. (2019). Optimal location and sizing of UPFC for optimal power flow in deregulated power system using hybrid algorithm. International Journal of Ambient Energy, 43(1), 1413–1419. https://doi.org/10.1080/01430750.2019.1707116
   Google Scholar
• Soolaki, M., & Arkat, J. (2018). Incorporating dynamic cellular manufacturing into strategic supply chain design. The International Journal of Advanced Manufacturing Technology, 95(5–8), 2429–2447. https://doi.org/10.1007/s00170-017-1346-2
   Web of Science ®Google Scholar
• Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
   Web of Science ®Google Scholar
• Subhashini, K. R., & Satapathy, J. K. (2017). Development of an enhanced ant lion optimization algorithm and its application in antenna array synthesis. Applied Soft Computing, 59, 153–173. https://doi.org/10.1016/j.asoc.2017.05.007
   Web of Science ®Google Scholar
• Suvitha, S., & Mathana, J. M. (2019). A novel automated MOALO algorithm aided RF low‐noise amplifier design for wireless applications. Concurrency and Computation: Practice and Experience, 31(14), e4915. https://doi.org/10.1002/cpe.4915
   Web of Science ®Google Scholar
• Talatahari, S. (2016). Optimum design of skeletal structures using ant lion optimizer. International Journal of Optimization in Civil Engineering, 6(1), 13–25. http://ijoce.iust.ac.ir/article-1-235-fa.html
   Google Scholar
• Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355–364). Springer, Berlin, Heidelberg.
   Google Scholar
• Thakkar, A., & Lohiya, R. (2020). Role of swarm and evolutionary algorithms for intrusion detection system: A survey. Swarm and Evolutionary Computation, 53, 100631. https://doi.org/10.1016/j.swevo.2019.100631
   Web of Science ®Google Scholar
• Tharwat, A., & Hassanien, A. E. (2018). Chaotic antlion algorithm for parameter optimization of support vector machine. Applied Intelligence, 48(3), 670–686. https://doi.org/10.1007/s10489-017-0994-0
   Web of Science ®Google Scholar
• Tian, T., Liu, C., Guo, Q., Yuan, Y., Li, W., & Yan, Q. (2018). An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies, 11(1), 95. https://doi.org/10.3390/en11010095
   Web of Science ®Google Scholar
• Tikhamarine, Y., Malik, A., Kumar, A., Souag-Gamane, D., & Kisi, O. (2019). Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological Sciences Journal, 64(15), 1824–1842. https://doi.org/10.1080/02626667.2019.1678750
   Web of Science ®Google Scholar
• Toz, M. (2019). An improved form of the ant lion optimization algorithm for image clustering problems. Turkish Journal of Electrical Engineering & Computer Sciences, 27(2), 1445–1460. https://doi.org/10.3906/elk-1703-240
   Web of Science ®Google Scholar
• Trivedi, I. N., Jangir, P., & Parmar, S. A. (2016). Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer. Cogent Engineering, 3(1), 1208942. https://doi.org/10.1080/23311916.2016.1208942
   Web of Science ®Google Scholar
• Ulker, E., & Tongur, V. (2017). Migrating birds optimization (MBO) algorithm to solve knapsack problem. Procedia Computer Science, 111, 71–76. https://doi.org/10.1016/j.procs.2017.06.012
   Google Scholar
• Umamaheswari, E., Ganesan, S., Abirami, M., & Subramanian, S. (2017). Cost effective integrated maintenance scheduling in power systems using ant lion optimizer. Energy Procedia, 117, 501–508. https://doi.org/10.1016/j.egypro.2017.05.176
   Google Scholar
• Van, T. P., Snášel, V., & Nguyen, T. T. (2020). Antlion optimization algorithm for optimal non-smooth economic load dispatch. International Journal of Electrical & Computer Engineering, 10(2), 1187–1199. http://doi.org/10.11591/ijece.v10i2.pp1187-1199
   Google Scholar
• Venkataraman, N. L., Kumar, R., & Shakeel, P. M. (2019). Ant lion optimized bufferless routing in the design of low power application specific network on chip. Circuits, Systems, and Signal Processing, 39, 961–976. https://doi.org/10.1007/s00034-019-01065-6
   Web of Science ®Google Scholar
• Venkataraman, N. L., & Kumar, R. (2019). Design and analysis of application specific network on chip for reliable custom topology. Computer Networks, 158, 69–76. https://doi.org/10.1016/j.comnet.2019.03.014
   Web of Science ®Google Scholar
• Verma, S., & Mukherjee, V. (2016). Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser. IET Generation, Transmission & Distribution, 10(10), 2548–2561. https://doi.org/10.1049/iet-gtd.2015.1555
   Web of Science ®Google Scholar
• Vinothkumar, T., & Deeba, K. (2020). Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models. Soft Computing, 24, 5345–5355. https://doi.org/10.1007/s00500-019-04292-w
   Web of Science ®Google Scholar
• Wang, J., Du, P., Lu, H., Yang, W., & Niu, T. (2018). An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting. Applied Soft Computing, 72, 321–337. https://doi.org/10.1016/j.asoc.2018.07.022
   Web of Science ®Google Scholar
• Wang, J., Bai, L., Wang, S., & Wang, C. (2019). Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system. Journal of Cleaner Production, 234, 54–70. https://doi.org/10.1016/j.jclepro.2019.06.201
   Web of Science ®Google Scholar
• Wang, M., Wu, C., Wang, L., Xiang, D., & Huang, X. (2019). A feature selection approach for hyperspectral image based on modified ant lion optimizer. Knowledge-Based Systems, 168, 39–48. https://doi.org/10.1016/j.knosys.2018.12.031
   Web of Science ®Google Scholar
• Wang, M., Gao, L., Huang, X., Jiang, Y., & Gao, X. (2019). A texture classification approach based on the integrated optimization for parameters and features of Gabor filter via hybrid ant lion optimizer. Applied Sciences, 9(11), 2173. https://doi.org/10.3390/app9112173
   Google Scholar
• Wang, J., Khishe, M., Kaveh, M., & Mohammadi, H. (2021). Binary Chimp Optimization Algorithm (BChOA): A new binary meta-heuristic for solving optimization problems. Cognitive Computation, 13(5), 1297–1316. https://doi.org/10.1007/s12559-021-09933-7
   Web of Science ®Google Scholar
• Wedyan, A., Whalley, J., & Narayanan, A. (2017). Hydrological cycle algorithm for continuous optimization problems. Journal of Optimization, 2017, 1–25/ 3828420. https://doi.org/10.1155/2017/3828420
   Google Scholar
• Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
   Google Scholar
• Wu, Y. K., Chang, H. Y., & Chang, S. M. (2017). Analysis and comparison for the unit commitment problem in a large-scale power system by using three meta-heuristic algorithms. Energy Procedia, 141, 423–427. https://doi.org/10.1016/j.egypro.2017.11.054
   Google Scholar
• Wu, Z., Yu, D., & Kang, X. (2017). Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Conversion and Management, 151, 107–115. https://doi.org/10.1016/j.enconman.2017.08.088
   Web of Science ®Google Scholar
• Wu, G., Mallipeddi, R., & Suganthan, P. N. (2019). Ensemble strategies for population-based optimization algorithms–A survey. Swarm and Evolutionary Computation, 44, 695–711. https://doi.org/10.1016/j.swevo.2018.08.015
   Web of Science ®Google Scholar
• Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330–343. https://doi.org/10.48550/arXiv.1005.2908
   Google Scholar
• Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Juan R. González, David Alejandro Pelta, Carlos Cruz, Germán Terrazas, Natalio Krasnogor, Eds. Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Springer.
   Google Scholar
• Yang, X. S. (2014). Swarm intelligence based algorithms: A critical analysis. Evolutionary Intelligence, 7(1), 17–28. https://doi.org/10.1007/s12065-013-0102-2
   Google Scholar
• Yang, X. S., Deb, S., & Fong, S. (2014). Metaheuristic algorithms: Optimal balance of intensification and diversification. Applied Mathematics & Information Sciences, 8(3), 977. https://doi.org/10.12785/amis/080306
   Google Scholar
• Yang, D., Miao, J., Zhang, F., Tao, J., Wang, G., & Shen, Y. (2019). Bearing fault diagnosis using a support vector machine optimized by an improved ant lion optimizer. Shock and Vibration, 2019, 1–20. https://doi.org/10.1155/2019/9303676
   Web of Science ®Google Scholar
• Yao, P., & Wang, H. (2017). Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Computing, 21(18), 5475–5488. https://doi.org/10.1007/s00500-016-2138-6
   Web of Science ®Google Scholar
• Yogarajan, G., & Revathi, T. (2018). Improved cluster based data gathering using ant lion optimization in wireless sensor networks. Wireless Personal Communications, 98(3), 2711–2731. https://doi.org/10.1007/s11277-017-4996-3
   Web of Science ®Google Scholar
• Yousri, D., AbdelAty, A. M., Radwan, A. G., Elwakil, A. S., & Psychalinos, C. (2019). Comprehensive comparison based on meta-heuristic algorithms for approximation of the fractional-order Laplacian sα as a weighted sum of first-order high-pass filters. Microelectronics Journal, 87, 110–120. https://doi.org/10.1016/j.mejo.2019.03.012
   Web of Science ®Google Scholar
• Yuan, X., Chen, C., Lei, X., Yuan, Y., & Adnan, R. M. (2018). Monthly runoff forecasting based on LSTM–ALO model. Stochastic Environmental Research and Risk Assessment, 32(8), 2199–2212. https://doi.org/10.1007/s00477-018-1560-y
   Web of Science ®Google Scholar
• Zawbaa, H. M., Emary, E., & Grosan, C. (2016). Feature selection via chaotic antlion optimization. PloS one, 11(3), e0150652. https://doi.org/10.1371/journal.pone.0150652
   PubMed Web of Science ®Google Scholar
• Zawbaa, H. M., Emary, E., Grosan, C., & Snasel, V. (2018). Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach. Swarm and Evolutionary Computation, 42, 29–42. https://doi.org/10.1016/j.swevo.2018.02.021
   Web of Science ®Google Scholar
• Zhao, S., Gao, L., Yu, D., & Tu, J. (2016). Ant lion optimizer with chaotic investigation mechanism for optimizing SVM parameters. Journal of Frontiers of Computer Science and Technology, 10(5), 722–731. http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.1506093
   Google Scholar
• Zheng, L., Wang, Z., Zhao, Z., Wang, J., & Du, W. (2019). Research of bearing fault diagnosis method based on multi-layer extreme learning machine optimized by novel ant lion algorithm. IEEE Access, 7, 89845–89856. https://doi.org/10.1109/ACCESS.2019.2926348
   Web of Science ®Google Scholar
• Zhenxing, Z. H. A. N. G., Rennong, Y. A. N. G., Huanyu, L. I., Yuhuan, F. A. N. G., Zhenyu, H. U. A. N. G., & Ying, Z. H. A. N. G. (2019). Antlion optimizer algorithm based on chaos search and its application. Journal of Systems Engineering and Electronics, 30(2), 352–365. https://doi.org/10.21629/JSEE.2019.02.14
   Web of Science ®Google Scholar
• Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49. https://doi.org/10.1016/j.swevo.2011.03.001
   Web of Science ®Google Scholar
• Zhu, J., Chen, H., Wu, G., Chen, L., & Li, H. (2019). Pressure point driven evolutionary algorithm for many-objective optimization. Swarm and Evolutionary Computation, 51, 100599. https://doi.org/10.1016/j.swevo.2019.100599
   Web of Science ®Google Scholar