Abstract
Cuckoo Search (CS) is the powerful optimization algorithm and has been researched recently. Cuckoo Search for Dynamic Environment (D-CS) has proposed and tested in dynamic environment with multi-modality and cyclically before. It was clear that has the hold capability and can find the optimal solutions in this environment. Although these experiments only provide the valuable results in this environment, D-CS not fully explored in dynamic environment with other dynamism. We investigate and discuss the find and hold capabilities of D-CS on dynamic environment with randomness. We employed the multi-modal dynamic function with randomness and applied D-CS into this environment. We compared D-CS with CS in terms of getting the better fitness. The experimental result shows the D-CS has the good hold capability on dynamic environment with randomness. Introducing the Local Solution Comparison strategy and Concurrent Solution Generating strategy help to get the hold and find capabilities on dynamic environment with randomness.
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References
Jamil, M., Zepernick, H.J.: 3 levy flights and global optimization. In: Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, p. 49 (2013)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)
Kaveh, A., Bakhshpoori, T.: Optimum design of steel frames using cuckoo search algorithm with lévy flights. Struct. Design Tall Spec. Build. 22(13), 1023–1036 (2013)
Ong, P.: Adaptive cuckoo search algorithm for unconstrained optimization. Sci. World J. 2014, 8 (2014)
Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput. Appl. 24(7–8), 1659–1669 (2014)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)
Takano, R., Harada, T., Sato, H., Takadama, K.: Artificial bee colony algorithm based on local information sharing in dynamic environment. In: Handa, H., Ishibuchi, H., Ong, Y.-S., Tan, K.C. (eds.) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems. PALO, vol. 1, pp. 627–641. Springer, Cham (2015). doi:10.1007/978-3-319-13359-1_48
Tein, L.H., Ramli, R.: Recent advancements of nurse scheduling models and a potential path. In: Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA 2010), pp. 395–409 (2010)
Umenai, Y., Uwano, F., Tajima, Y., Nakata, M., Sato, H., Takadama, K.: A modified cuckoo search algorithm for dynamic optimization problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1757–1764. IEEE (2016)
Xin-She, Y., Suash, D.: Cuckoo search via levy flight. In: World Congress on Nature and Biologically Inspired Computing, NaBIC 2009, pp. 210–214 (2009)
Yang, X.S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)
Yang, X.S., Karamanoglu, M.: Swarm intelligence and bio-inspired computation: an overview. In: Swarm Intelligence and Bio-inspired Computation-Tehory and Applications, pp. 3–23. Elsevier (2013)
Zaw, M.M., Mon, E.E.: Web document clustering using cuckoo search clustering algorithm based on levy flight. Int. J. Innov. Appl. Stud. 4(1), 182–188 (2013)
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Umenai, Y., Uwano, F., Sato, H., Takadama, K. (2017). Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_62
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DOI: https://doi.org/10.1007/978-3-319-61824-1_62
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