Nature-Inspired Algorithms and Applications. Группа авторов
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Название: Nature-Inspired Algorithms and Applications

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119681663

isbn:

СКАЧАТЬ 4. Particle swarm optimization algorithm Combination with a back engendering calculation, to prepare a neural system framework structure, multi-target optimization, classification, image clustering and image clustering, image processing, automated applications, dynamic, pattern recognition, image segmentation, robotic applications, time frequency analysis, decision-making, simulation, and identification 5. Harmony search algorithm Power systems, power systems, transportation, medical science and robotics, industry and signal and image processing 6. Artificial bee colony algorithm Problem of medical pattern classification, network reconfiguration, minimum spanning tree, train neural networks, radial distribution system of network reconfiguration, and train neural networks 7. Firefly algorithm Semantic web composition, classification and clustering problems, neural network, fault detection, digital image compression, feature selection, digital image processing, scheduling problems, and traveling salesman problem 8. Bat algorithm Image processing, clustering, classification, data mining, continuous optimization, problem inverse and estimation of parameter, combination scheduling and optimization, and fuzzy logic

      The working of EHO is based on that every elephant in clan is updated by utilizing group data through clan by the procedure of updating, and afterward, the poorest elephant is supplanted by randomly produced elephant individual through the procedure of updating. EHO can discover much improved solutions on more problems of benchmark. Problems of benchmark are a lot of different types of problem of optimization that comprises of different kinds of aptitudes that utilized in testing and the estimation is verified and described. Then, the execution of estimation enhances the algorithm under various ecological conditions.

      1. Siddique, N. and Adeli, H., Nature-Inspired Computing: An Overview and Some Future Directions. Cognit. Comput., 7, 706–714, 2015.

      3. Fan, X., Sayers, W., Zhang, S. et al., Review and Classification of Bio-inspired Algorithms and Their Applications. J. Bionic Eng., 17, 611–631, 2020, https://doi.org/10.1007/s42235-020-0049-9.

      4. Nguyen, B.H., Xue, B., Zhang, M., A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput., 54, 100663, 2020.

      5. Neri, F. and Cotta, C., Memetic algorithms and memetic computing optimization: A literature review. Swarm Evol. Comput., 2, 1–14, 2012, 10.1016/j. swevo.2011.11.003.

      6. Albuquerque, I.M.R., Nguyen, B.H., Xue, B., Zhang, M., A Novel Genetic Algorithm Approach to Simultaneous Feature Selection and Instance Selection. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, pp. 616–623, 2020.

      7. Ding, X. 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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