Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition

References

• Adam, S. P., Alexandropoulos, S. N., Pardalos, P. M., & Vrahatis, M. N. (2019). No Free Lunch Theorem : A Review. Approximation and Optimization, 57–82. https://doi.org/10.1007/978-3-030-12767-1_5
   Google Scholar
• Beyer, H., & Schwefel, H. (2002). Evolution strategies – A comprehensive introduction. Natural Computing, 1(1), 3–52. https://doi.org/10.1023/A:1015059928466
   Google Scholar
• Bhanu, B. (1986). Automatic Target Recognition : State of the Art Survey. IEEE Transactions on Aerospace and Electronic Systems, 4(4), 364–379. https://doi.org/10.1109/TAES.1986.310772
   Google Scholar
• Cano, A., Zafra, A., & Ventura, S. (2011). An EP Algorithm for Learning Highly Interpretable Classifiers. 11th International Conference on Intelligent Systems Design and Applications 2011. Cordoba, Spain.
   Google Scholar
• Hodavandi, E., Mostafayi, S., & Soltani, P. (2018). A Grey Wolf Optimizer-Based Neural Network Coupled with Response Surface Method for Modeling the Strength of Siro-Spun Yarn in Spinning Mills. Applied Soft Computing Journal, 72, 1–13. https://doi.org/10.1016/j.asoc.2018.07.055
   Web of Science ®Google Scholar
• Feng, Y., Deb, S., Wang, G., & Alavi, A. H. (2020). Monarch Butterfly Optimization: A Comprehensive Review. Expert Systems With Applications, 168. https://doi.org/10.1016/j.eswa.2020.114418
   Google Scholar
• Formato, R. (2014). Central Force Optimization : A New Metaheuristic with Applications in Applied Electromagnetics. 77, 425–491. https://doi.org/10.2528/PIER07082403
   Google Scholar
• Glover, F., & Laguna, M. (2008). Principles of Tabu Search. https://doi.org/10.1007/978-1-4615-6089-0
   Google Scholar
• Gueorguieva, N., Valova, I., & Georgiev, G. (2007). Learning and data clustering with an RBF-based spiking neuron network. Experimental & Theoretical Artificial Intelligence, 73–86. https://doi.org/10.1080/09528130600552888
   Google Scholar
• Hamza, F., Abderazek, H., Lakhdar, S., Ferhat, D., & Yıldız, A. R. (2018). Optimum Design of Cam-Roller Follower Mechanism Using a New Evolutionary Algorithm. The International Journal of Advanced Manufacturing Technology Volume, 99(5–8), 1267–1282. https://doi.org/10.1007/s00170-018-2543-3
   Web of Science ®Google Scholar
• Heidari, A. A., Mirjalili, S. A., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028
   Web of Science ®Google Scholar
• Ho, Y. C., & Pepyne, D. L. (2002). Simple Explanation of the No-Free-Lunch Theorem and Its Implications. Optimization Theory and Applications, 115(3), 549–570. https://doi.org/10.1023/A:1021251113462
   Web of Science ®Google Scholar
• Karaboga, D., Akay, B., & Ozturk, C. (2007). Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural. Modeling Decisions for Artificial Intelligence, 318–329. https://doi.org/10.1007/978-3-540-73729-2_30
   Google Scholar
• Kashan, A. H. (2014). League Championship Algorithm (LCA): An Algorithm for Global Optimization Inspired by Sport Championships. Applied Soft Computing Journal, 16, 171–200. https://doi.org/10.1016/j.asoc.2013.12.005
   Web of Science ®Google Scholar
• Kashani, A. R., Gandomi, A. H., & Mousavi, M. (2016). Geoscience Frontiers Imperialistic Competitive Algorithm : A Metaheuristic Algorithm for Locating the Critical Slip Surface in 2-Dimensional Soil Slopes. Geoscience Frontiers, 7(1), 83–89. https://doi.org/10.1016/j.gsf.2014.11.005
   Web of Science ®Google Scholar
• Kennedy, J., & Eberhart, R. (1995). “Particle Swarm Optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, IEEE, Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968
   Google Scholar
• Khishe, M., & Mosavi, M. R. (2019). Improved Whale Trainer for Sonar Datasets Classification Using Neural Network. Applied Acoustics, 154, 176–192. https://doi.org/10.1016/j.apacoust.2019.05.006
   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
• Khishe, M., & Safari, A. (2019). Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm. Wireless Personal Communications, 108(4), 2241–2260. https://doi.org/10.1007/s11277-019-06520-w
   Web of Science ®Google Scholar
• Koh, C. S., Mohammed, O. H., & Hahn, S. (1994). Detection of Magnetic Body Using Artificial Neural Network with Modified Simulated Annealing. IEEE Transactions on Magnetics, 30(5), 3644–3647. https://doi.org/10.1109/20.312730
   Web of Science ®Google Scholar
• Kurtuluş, E., Yıldız, A. R., Sait, S. M., & Bureerat, S. (2020). A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails. Materials Testing, 62(3), 251–260. https://doi.org/10.3139/120.111478
   Web of Science ®Google Scholar
• Lai, K. H., Zainuddin, Z., & Ong, P. (2017). A Study on the Performance Comparison of Metaheuristic Algorithms on the Learning of Neural Networks. AIP Conference Proceedings, 1870. https://doi.org/10.1063/1.4995871
   Google Scholar
• Lakhwani, K. (2020). Big Data Classification Techniques: A Systematic Literature. Journal of Natural Remedies, 21(2), 1–13.
   Google Scholar
• Li, J., Lei, H., Alavi, A. H., & Wang, G. (2020). Elephant Herding Optimization : Variants, Hybrids, and Applications. Mathematics, 8(9), 1–25. https://doi.org/10.3390/math8091415
   Web of Science ®Google Scholar
• Li, W., Wang, G., & Gandomi, A. H. (2021). A Survey of Learning ‑ Based Intelligent Optimization Algorithms. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-021-09562-1
   Google Scholar
• Liu, J., Zhou, Z., & Zeng, X. (2019). Multi-Static Active Sonar Target Recognition Method Based on Bionic Signal. 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), Weihai, China. https://doi.org/10.1109/ICICSP48821.2019.8958532
   Google Scholar
• Meirelles, G., Brentan, B., Izquierdo, J., & Luvizotto, J. E. (2020). Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems. Processes, 8(1–19), 980. https://doi.org/10.3390/pr8080980
   Google Scholar
• Meng, Z., Li, G., Wang, X., Sait, S. M., & Yıldız, A. R. (2020). A Comparative Study of Metaheuristic Algorithms for Reliability‑Based Design Optimization Problems. Archives of Computational Methods in Engineering, 28(3), 1853–1869. https://doi.org/10.1007/s11831-020-09443-z
   Web of Science ®Google Scholar
• Mirjalili, S., Hashim, S. Z. M., & Moradian Sardroudi, H. (2012). Training Feedforward Neural Networks Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm. Applied Mathematics and Computation, 218(22), 11125–11137. https://doi.org/10.1016/j.amc.2012.04.069
   Web of Science ®Google Scholar
• Mirjalili, S., & Lewis, A. (2016). Advances in Engineering Software The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, M. S., & Lewis, A. (2014). Let a Biogeography-Based Optimizer Train Your Multi-Layer Perceptron. Information Sciences, 269, 188–209. https://doi.org/10.1016/j.ins.2014.01.038
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S., & Lewis, A. (2014). Advances in Engineering Software 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., & Sadiq, A. S. (2011). Magnetic Optimization Algorithm for training Multi Layer Perceptron. 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi’an, China. https://doi.org/10.1109/ICCSN.2011.6014845
   Google Scholar
• Montana, D. J., & Davis, L. (1989). Training Feedforward Neural Networks Using Genetic Algorithms. Proceedings of the 11th International Joint Conference on Artificial Intelligence, 1(89), 762–767. http://dl.acm.org/citation.cfm?id=1623755.1623876
   Google Scholar
• Mosavi, M. R., Khishe, M., Naseri, M. J., Parvizi, G. R., & Ayat, M. (2019). Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset. Archives of Acoustics, 44(1), 137–151. https://doi.org/10.24425/aoa.2019.126360
   Web of Science ®Google Scholar
• Panagant, N., Pholdee, N., Bureerat, N., Kaen, K., Yıldız, A. R., & Sait, S. M. (2021). Seagull optimization algorithm for solving real-world design optimization problems. Material Testing, 62(6), 640–644. https://doi.org/10.3139/120.111529
   Web of Science ®Google Scholar
• Pendharkar, P. C. (2011). A Hybrid Radial Basis Function and Data Envelopment Analysis Neural Network for Classification. Computers and Operations Research, 38(1), 256–266. https://doi.org/10.1016/j.cor.2010.05.001
   Web of Science ®Google Scholar
• Pereira, L. A. M., Rodrigues, D., Ribeiro, B. P., Papa, J. P., & Weber, S. A. T. (2014). Social-Spider Optimization-Based Artificial Neural Networks Training and Its Applications for Parkinson’s Disease Identification. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 14–17. https://doi.org/10.1109/CBMS.2014.25
   Google Scholar
• Pu, X., Chen, S., Yu, X., & Zhang, L. (2018). Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge. Scientific Programming, 2018, 1–7. https://doi.org/10.1155/2018/2943290
   Web of Science ®Google Scholar
• Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Computer-Aided Design Teaching – Learning-Based Optimization : A Novel Method for Constrained Mechanical Design Optimization Problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015
   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
• Sánchez, D., Melin, P., & Castillo, O. (2017). A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition. Computational Intelligence and Neuroscience, 1-26. https://doi.org/10.1155/2017/418051
   Google Scholar
• Sarangkum, R., Wansasueb, K., Panagant, N., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2019). Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation. International Journal of Vehicle Design, 80(2/3/4), 162–175. https://doi.org/10.1504/IJVD.2019.109864
   Web of Science ®Google Scholar
• Sh., L., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. A. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
   Web of Science ®Google Scholar
• Wang, G. (2018). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151–164. https://doi.org/10.1007/s12293-016-0212-3
   Web of Science ®Google Scholar
• Wang, G., Deb, S., & Coelho, L. D. S. (2018). Earthworm optimisation algorithm: A bio-inspired metaheuristic algorithm for global optimisation problems. International Journal of Bio-Inspired Computation, 12(1), 1–22. https://doi.org/10.1504/IJBIC.2018.093328
   Web of Science ®Google Scholar
• Wang, G., Gandomi, A. H., Alavi, A. H., & Gong, D. (2017). A Comprehensive Review of Krill Herd Algorithm : Variants, Hybrids and Applications. Artificial Intelligence Review, 51(1), 119–148. https://doi.org/10.1007/s10462-017-9559-1
   Web of Science ®Google Scholar
• Wang, P., Yu, X., & Lü, J. (2014). Identification and Evolution of Structurally Dominant Nodes in Protein-Protein Interaction Networks. IEEE Transactions on Biomedical Circuits and Systems, 8(1), 87–97. https://doi.org/10.1109/TBCAS.2014.2303160
   PubMed Web of Science ®Google Scholar
• Yıldız, A. B. S., Pholdee, N., Bureerat, S., Yıldız, A. R., & Sait, S. M. (2020). Sine-cosine optimization algorithm for the conceptual design of automobile components. Material Testing, 62(7), 744–748. https://doi.org/10.3139/120.111541
   Web of Science ®Google Scholar
• Yildiz, A. R. (2019). A novel hybrid whale–Nelder–Mead algorithm for optimization of design and manufacturing problems. The International Journal of Advanced Manufacturing Technology, 105(5091–5104), 5091–5104. h ttps://d o i.o r g/1 0.1007%2Fs00170-019-04532-1
   Google Scholar
• Yıldız, A. R., Özkaya, H., Yıldız, M., Bureerat, S., Yıldız, B. S., & Sait, S. M. (2020). The equilibrium optimization algorithm and the response surface-based metamodel for optimal structural design of vehicle components. Material Testing, 62(5), 492–496. https://doi.org/10.3139/120.111509
   Web of Science ®Google Scholar
• Yıldız, A. R., Yıldız, B. S., Sait, S. M., Bureerat, S., & Pholdee, N. (2019). A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems. Material Testing, 61(8), 735–743. https://doi.org/10.3139/120.111378
   Web of Science ®Google Scholar
• Yıldız, A. R., Yıldız, B. S., Sait, S. M., & Li, X. (2019). The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations. Material Testing, 61(8), 725–733. https://doi.org/10.3139/120.111377
   Web of Science ®Google Scholar
• Yıldız, B. S. (2021). The spotted hyena optimization algorithm for weight-reduction of automobile brake components. Material Testing, 62(4), 383–388. https://doi.org/10.3139/120.111495
   Web of Science ®Google Scholar
• Yıldız, B. S., Yıldız, A. R., Albak, E. A., Abderazek, H., Sait, S. M., & Bureerat, S. (2021). Butterfly optimization algorithm for optimum shape design of automobile suspension components. Material Testing, 62(4), 365–370. https://doi.org/10.3139/120.111492
   Web of Science ®Google Scholar
• Yıldız, B. S., Yıldız, A. R., Pholdee, N., Bureerat, S., Sait, S. M., & Patel, V. (2020). The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components. Material Testing, 62(3), 261–264. https://doi.org/10.3139/120.111479
   Web of Science ®Google Scholar