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Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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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

  1. Jamil, M., Zepernick, H.J.: 3 levy flights and global optimization. In: Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, p. 49 (2013)

    Google Scholar 

  2. 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)

    Article  MathSciNet  MATH  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Ong, P.: Adaptive cuckoo search algorithm for unconstrained optimization. Sci. World J. 2014, 8 (2014)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  7. 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

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. Yang, X.S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

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Authors and Affiliations

  1. The University of Electro-Communications, Tokyo, Japan

    Yuta Umenai, Fumito Uwano, Hiroyuki Sato & Keiki Takadama

Authors
  1. Yuta Umenai
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  2. Fumito Uwano
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  3. Hiroyuki Sato
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  4. Keiki Takadama
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Corresponding author

Correspondence to Yuta Umenai .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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