Nature-Inspired Algorithms and Applications. Группа авторов

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Nature-Inspired Algorithms and Applications - Группа авторов


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et al., An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery. IEEE Access, 8, 25789– 25799, 2020.

      8. Xie, X.-F., Zhang, W.-J., Yang, Z.-L., Social cognitive optimization for nonlinear programming problems. Proceedings. International Conference on Machine Learning and Cybernetics, Beijing, China, vol. 2, pp. 779–783, 2002.

      9. Pham, D.T., Afshin, G., Ebubekir, K., Sameh, O., Sahra, R., Zaidi, M., The Bees Algorithm - A Novel Tool for Complex Optimisation Problems. Proceedings of IPROMS 2006 Conference, 10.1016/B978-008045157-2/50081-X.

      10. He, S., Wu, Q.H., Saunders, J.R., Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior. IEEE Trans. Evol. Comput., 13, 5, 973–990, Oct. 2009.

      11. Rabanal, P., Rodríguez, I., Rubio, F., Solving Dynamic TSP by Using River Formation Dynamics. 2008 Fourth International Conference on Natural Computation, Jinan, pp. 246–250, 2008.

      12. Li, J., Guo, L., Li, Y., Liu, C., Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems. Mathematics, 7, 395, 2019, 10.3390/math7050395.

      13. Almufti, S.M., Asaad, R.R., Salim, B.W., Review on Elephant Herding Optimization Algorithm Performance in Solving Optimization Problems. Int. J. Eng. Technol., 7, 6109–6114, 2018, 10.14419/ijet.v7i4. 23127.

      14. Ma, L., Wang, R., Chen, Y., The Social Cognitive Optimization Algorithm: Modifiability and Application. 2010 International Conference on E-Product E-Service and E-Entertainment, Henan, pp. 1–4, 2010.

      15. Redlarski, G., Pałkowski, A., Dąbkowski, M., Using River Formation Dynamics Algorithm in Mobile Robot Navigation. Solid State Phenom., 198, 138–143, 2013, 10.4028/www.scientific.net/SSP.198.138.

      17. Liu, F., Xu, X.-T., Li, L.-J., Wu, Q.H., The Group Search Optimizer and its Application on Truss Structure Design. 2008 Fourth International Conference on Natural Computation, Jinan, pp. 688–692, 2008.

      18. Joong, K., Harmony Search Algorithm: A Unique Music-inspired Algorithm. Proc. Eng., 154, 1401–1405, 2016, 10.1016/j.proeng.2016.07.510.

      19. Yang, X.-S., Harmony Search as a Metaheuristic Algorithm. Stud. Comput. Intell., 191, pp.1–14, 2010, 10.1007/978-3-642-00185-7_1.

      20. Gao, X.Z., Govindasamy, V., Xu, H., Xianjia, W., Kai, Z., Harmony Search Method: Theory and Applications. Comput. Intell. Neurosci., 1–10, Vol 2015, 10.1155/2015/258491.

      21. Husseinzadeh Kashan, A., A new metaheuristic for optimization: Optics inspired optimization (OIO). Comput. Oper. Res., 55, pp.99–125 2014, 10.1016/j.cor.2014.10.011.

      22. Redlarski, G., Dabkowski, M., Pałkowski, A., Generating optimal paths in dynamic environments using River Formation Dynamics algorithm.

      23. Doğan, B., A Modified Vortex Search Algorithm for Numerical Function Optimization. Int. J. Artif. Intell. Appl., 7, 37–54, 2016, 10.5121/ijaia.2016.7304.

      24. Sajedi, H. and Razavi, S.F., MVSA: Multiple vortex search algorithm. 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, pp. 000169–000174, 2016. J. Comput. Sci., 20, 8–16, 2017, 10.1016/j.jocs.2017.03.002.

      1 *Corresponding author: [email protected]

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