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An improved cuckoo search algorithm for global optimization

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Abstract

Cuckoo search (CS) algorithm is a classical swarm intelligence algorithm widely used in a variety of engineering optimization problems. However, its search accuracy and convergence speed still have a lot of room for improvement. In this paper, an improved version of the CS algorithm based on intelligent perception strategy, adaptive invasive weed optimization (AIWO), and elite cross strategy, called IIC-CS is proposed. Firstly, the intelligent perception strategy can update the value according to the searching state. Moreover, the CS is hybridized with the AIWO to improve the searching performance of the algorithm. Additionally, the elite cross strategy is employed to enhance the exploration capability and exploitation capability of the algorithm. Combining the improvements of these three methods, the performance of the CS algorithm is significantly improved. Meanwhile, 23 classical benchmark functions, some CEC2014 and CEC2018 benchmark functions are used to test the search accuracy and convergence rate of the IIC-CS. Furthermore, some classical or state-of-the-art algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), bat algorithm (BA), ant lion optimizer (ALO) and cuckoo search (CS) algorithm, invasive weed optimization (IWO), integrated cuckoo search optimizer (ICSO) and improved island cuckoo search (iCSPM2) are used to make comparisons. Through the statistical results of the experiments, we find that the IIC-CS algorithm can achieve better results on most benchmark functions compared to other algorithms, thus demonstrating the effectiveness of the improvements and the superiority of the IIC-CS algorithm.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Houssein, E.H., Saad, M.R., Hashim, F.A., et al.: Levy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 94, 103731 (2020)

    Article  Google Scholar 

  2. Hemeida, A.M., Alkhalaf, S., Mady, A., et al.: Implementation of nature-inspired optimization algorithms in some data mining tasks. Ain Shams Eng. J. 11(2), 309–318 (2020)

    Article  Google Scholar 

  3. Yildiz, B.S., Yildiz, A.R.: The Harris Hawks optimization algorithm, Salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components. Mater. Test. 61(8), 744–748 (2019)

    Article  Google Scholar 

  4. Zhang, R.: Sports action recognition based on particle swarm optimization neural networks. Wirel. Commun. Mob. Comput. 2022, 6912315 (2022)

    Google Scholar 

  5. Menezes, B.A.D., Kuchen, H., Neto, F.B.D.: Parallelization of swarm intelligence algorithms: literature review. Int. J. Parallel Program. 50(5–6), 486–514 (2022)

    Article  Google Scholar 

  6. Li, Q., Li, S.Y.: Optimization of artificial CNN based on swarm intelligence algorithm. J. Intell. Fuzzy Syst. 40(4), 6163–6173 (2021)

    Article  Google Scholar 

  7. Schaffer, J.D., Caruana, R., Eshelman, L.J., et al.: A study of control parameters affecting online performance of genetic algorithms for function optimization. In: International Conference on Genetic Algorithms, 1989. Morgan Kaufmann Publishers, Inc. (1989)

  8. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS95 Sixth International Symposium on Micro Machine and Human Science, 2002. IEEE (2002)

  9. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

  10. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  11. Zamani, H., Nadimi-Shahraki, M.H., Gandomi, A.H.: Starling murmuration optimizer: a novel bio-inspired algorithm for global and engineering optimization. Comput. Methods Appl. Mech. Eng. 392, 114616 (2022)

    Article  MathSciNet  Google Scholar 

  12. Zamani, H., Nadimi-Shahraki, M.H., Gandomi, A.H.: QANA: quantum-based avian navigation optimizer algorithm. Eng. Appl. Artif. Intell. 104, 104314 (2021)

    Article  Google Scholar 

  13. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)

    Article  Google Scholar 

  14. Zamani, H., Nadimi-Shahraki, M.H.: An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis. Biomed. Signal Process. Control 90, 105879 (2024)

    Article  Google Scholar 

  15. Nadimi-Shahraki, M.H., Asghari Varzaneh, Z., Zamani, H., et al.: Binary starling murmuration optimizer algorithm to select effective features from medical data. Appl. Sci. 13(1), 564 (2022)

    Article  Google Scholar 

  16. Nadimi-Shahraki, M.H., Fatahi, A., Zamani, H., et al.: Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data. Mathematics 10(15), 2770 (2022)

    Article  Google Scholar 

  17. Fatahi, A., Nadimi-Shahraki, M.H., Zamani, H.: An improved binary quantum-based avian navigation optimizer algorithm to select effective feature subset from medical data: a COVID-19 case study. J. Bionic Eng. 21(5), 1–21 (2023)

    Google Scholar 

  18. Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)

    Google Scholar 

  19. Ban, M., Moskat, C., Barta, Z., et al.: Simultaneous viewing of own and parasitic eggs is not required for egg rejection by a cuckoo host. Behav. Ecol. 24(4), 1014–1021 (2013)

    Article  Google Scholar 

  20. Senthilnath, J., Das, V., Omkar, S.N., et al.: Clustering using Levy flight cuckoo search. In: Advances in Intelligent Systems and Computing, 2013 (2013)

  21. Valian, E., Mohanna, S., Tavakoli, S.: Improved cuckoo search algorithm for global optimization. Int. J. Commun. Inf. Technol. 1(1), 31–44 (2011)

    Google Scholar 

  22. Walton, S., Hassan, O., Morgan, K., et al.: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9), 710–718 (2011)

    Article  Google Scholar 

  23. Layeb, A.: A novel quantum inspired cuckoo search for knapsack problems. Int. J. Bio-inspired Comput. 3(5), 297–305 (2012)

    Article  Google Scholar 

  24. Ghodrati, A., Lotfi, S.: A hybrid CS/PSO algorithm for global optimization. In: Proceedings of the 4th Asian Conference on Intelligence information and Database Systems, Kaohsiung, China, 2012, pp. :89–98 (2012)

  25. Salimi, H., Giveki, D., Soltanshahi, M.A., et al.: Extended mixture of MLP experts by hybrid of conjugate gradient method and modified cuckoo search. Int. J. Artif. Intell. Appl. 3(1), 107–113 (2012)

    Google Scholar 

  26. He X S, Wang F, Wang Y, et al. Global convergence analysis of cuckoo search using Markov theory. In: Nature-Inspired Algorithms and Applied Optimization, Studies in Computational Intelligence, vol. 744, pp. 53–67. Springer, Cham (2018)

  27. Wang, G.G., Deb, S., Gandomi, A.H., et al.: Chaotic cuckoo search. Soft. Comput. 20(9), 3349–3362 (2016)

    Article  Google Scholar 

  28. Huang, L., Ding, S., Yu, S.H., et al.: Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl. Math. Model. 40(5–6), 3860–3875 (2016)

    Article  MathSciNet  Google Scholar 

  29. Boushaki, S.I., Kamel, N., Bendjeghaba, O.: A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst. Appl. 96, 358–372 (2018)

    Article  Google Scholar 

  30. Cuong-Le, T., Minh, H.L., Khatir, S., et al.: A novel version of Cuckoo search algorithm for solving optimization problems. Expert Syst. Appl. 186, 115669 (2021)

    Article  Google Scholar 

  31. Li, J., Li, Y.X., Tian, S.S., et al.: An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput. Appl. 32(16), 11967–11997 (2020)

    Article  Google Scholar 

  32. Abed-Alguni, B.H., Alawad, N.A., Barhoush, M., et al.: Exploratory cuckoo search for solving single-objective optimization problems. Soft. Comput. 25(15), 10167–10180 (2021)

    Article  Google Scholar 

  33. Tsipianitis, A., Tsompanakis, Y.: Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization. Adv. Eng. Softw. 149, 102865 (2020)

    Article  Google Scholar 

  34. Li, J., **ao, D.D., Zhang, T., et al.: Multi-swarm cuckoo search algorithm with Q-learning model. Comput. J. 61(1), 108–131 (2021)

    Article  MathSciNet  Google Scholar 

  35. Qi, X.B., Yuan, Z.H., Song, Y.: An integrated cuckoo search optimizer for single and multi-objective optimization problems. Peer J Comput. Sci. 7, e370 (2021)

    Article  Google Scholar 

  36. Abed-Alguni, B.H.: Island-based cuckoo search with highly disruptive polynomial mutation. Int. J. Artif. Intell. 17(1), 57–82 (2019)

    Google Scholar 

  37. Abed-Alguni, B.H., Paul, D.: Island-based Cuckoo Search with elite opposition-based learning and multiple mutation methods for solving optimization problems. Soft. Comput. 26(7), 3293–3312 (2022)

    Article  Google Scholar 

  38. 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 

  39. Chandrasekaran, K., Simon, S.P.: Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol. Comput. 5, 1–16 (2012)

    Article  Google Scholar 

  40. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)

    Article  Google Scholar 

  41. Yue, X.F., Zhang, H.B., Yu, H.Y.: A hybrid grasshopper optimization algorithm with invasive weed for global optimization. IEEE Access 8, 5928–5960 (2020)

    Article  Google Scholar 

  42. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report 635(2). Nanyang Technological University, Singapore (2013)

  43. Awad, N.H., Ali, M.Z., Liang, J.J., et al.: Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization. Technical Report, pp. 1–34. Nanyang Technological University Singapore (2016)

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Funding

This work was supported by State Grid Jilin Electric Power Research Institute (No. 522342220008).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun, Jilin, China

    Yunsheng Tian, Hongbo Zhang, Juan Zhu & **aofeng Yue

  2. State Grid Jilin Electric Power Research Institute, Changchun, China

    Dan Zhang

Authors
  1. Yunsheng Tian
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  2. Dan Zhang
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  3. Hongbo Zhang
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  4. Juan Zhu
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  5. **aofeng Yue
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Contributions

All authors contributed to the study conception and design. Yunsheng Tian carried out the definition of intellectual content, literature search, data acquisition, data analysis and manuscript preparation. Hongbo Zhang provided assistance for data acquisition, data analysis and manuscript editing. Dan Zhang, Juan Zhu and **aofeng Yue performed manuscript review. All authors have read and approved the content of the manuscript.

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Correspondence to **aofeng Yue.

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Appendix: Unimodal, multimodal, fixed-dimension multimodal test functions, partial CEC2014 and CEC2018 benchmark functions

Appendix: Unimodal, multimodal, fixed-dimension multimodal test functions, partial CEC2014 and CEC2018 benchmark functions

See Tables 13, 14, 15, 16, and 17.

Table 13 Unimodal test functions
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Table 14 Multimodal test functions
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Table 15 Fixed-dimension multimodal test functions
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Table 16 Partial CEC2014 benchmark functions
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Table 17 Partial CEC2018 benchmark functions
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Tian, Y., Zhang, D., Zhang, H. et al. An improved cuckoo search algorithm for global optimization. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04410-w

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