布谷鸟优化算法的20个参考文献

以下是布谷鸟优化算法的20个参考文献：

1. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.

2. Yang, X. S. (2011). Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation, 3(5), 267-274.

3. Chen, T., & Li, J. (2014). A novel hybrid algorithm based on bat algorithm and differential evolution for global optimization. Applied Soft Computing, 22, 114-121.

4. Wang, G. G., Deb, S., & Coelho, L. D. (2015). Elephant herd optimization. In Swarm, evolutionary, and memetic computing (pp. 415-423). Springer, Cham.

5. Chen, T., & Li, J. (2015). A novel bat algorithm with differential evolution for global optimization. Soft Computing, 19(3), 643-662.

6. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17-35.

7. Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing, 210-214.

8. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.

9. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Applied Intelligence, 43(1), 150-161.

10. Shirvani Boroujeni, M., & Mahdavi, M. (2015). A new metaheuristic optimization algorithm inspired by barley seed germination process. Computers & Industrial Engineering, 88, 478-491.

11. Sabagh Moftakhar, F., & Mahdavi, M. (2015). A new metaheuristic optimization algorithm inspired by sand dune formations: Dune algorithm. Expert Systems with Applications, 42(21), 7903-7913.

12. Yang, X. S. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, 240-249.

13. Zhang, J., & Sanderson, A. C. (2011). JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945-958.

14. Wang, G. G., & Deb, S. (2015). Firefly algorithm with parameter adaptation. Soft Computing, 19(9), 2499-2513.

15. Gao, X., & Wang, S. (2013). A new particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing, 13(1), 493-504.

16. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281-295.

17. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942-1948.

18. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43.

19. Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation, 69-73.

20. Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.