Opposition Based Local Escaping Marine Predators Algorithm for Continuous Optimization
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69257-4_9
Reference16 articles.
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2. Faramarzi, A., Heidarinejad, M., Mirjalili, S., Gandomi, A.H.: Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst. Appl. 152, 113377 (2020)
3. Zhong, K., Luo, Q., Zhou, Y., Jiang, M.: TLMPA: teaching-learning-based Marine Predators algorithm. Aims Math. 6(2), 1395–1442 (2021)
4. Hans, R., Kaur, H.: Opposition-based enhanced grey wolf optimization algorithm for feature selection in breast density classification. Int. J. Mach. Learn. Comput. 10(3), 458–464 (2020)
5. Jangir, P., Buch, H., Mirjalili, S., Manoharan, P.: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evol. Intell. 16(1), 169–195 (2023)
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