[1] Kirkpatrick S, Gelatt C D Jr, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220(4598): 671-680 doi: 10.1126/science.220.4598.671
[2] Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
[3] Dorigo M, Stützle T. Ant Colony Optimization. Cambridge, MA: MIT Press, 2004.
[4] Kennedy J, Eberhart R C. Swarm Intelligence. San Francisco, CA: Morgan Kaufmann, 2001.
[5] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007, 39(3): 459-471 doi: 10.1007/s10898-007-9149-x
[6] Yang X S. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2010, 2(2): 78-84 doi: 10.1504/IJBIC.2010.032124
[7] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46-61 doi: 10.1016/j.advengsoft.2013.12.007
[8] Wang G G, Deb S, Cui Z H. Monarch Butterfly Optimization, Neural Computing and Applications. New York, NY, USA: Springer, 2015. 1-20
[9] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51-67 doi: 10.1016/j.advengsoft.2016.01.008
[10] 肖辉辉, 万常选, 段艳明, 谭黔林.基于引力搜索机制的花朵授粉算法.自动化学报, 2017, 43(4): 576-594 doi: 10.16383/j.aas.2017.c160146

Xiao Hui-Hui, Wan Chang-Xuan, Duan Yan-Ming, Tan Qian-Lin. Flower pollination algorithm based on gravity search mechanism. Acta Automatica Sinica, 2017, 43(4): 576-594 doi: 10.16383/j.aas.2017.c160146
[11] Dhiman G, Kumar V. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017, 114: 48-70 doi: 10.1016/j.advengsoft.2017.05.014
[12] Slowik A, Kwasnicka H. Nature inspired methods and their industry applications—swarm intelligence algorithms. IEEE Transactions on Industrial Informatics, 2018, 14(3): 1004-1015 doi: 10.1109/TII.2017.2786782
[13] 冯友兰.中国哲学简史.北京:新世界出版社, 2004.

Feng You-Lan. A Brief History of Chinese Philosophy. Beijing: New World Press, 2004.
[14] Tam S C, Tam H K, Tam L M, Zhang T. A new optimization method, the algorithm of changes, for Bin Packing Problem. In: Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications. Changsha, China: IEEE, 2010. 994-999
[15] Zhao R. Survivable topology design of hybrid fiber-VDSL access networks with a novel metaheuristic. In: Proceedings of the 5th Advanced International Conference on Telecommunications. Venice/Mestre, Italy, 2009.
[16] Cui Y H, Guo R K, Guo D. A naïve five-element string algorithm. Journal of Software, 2009, 4(9): 925-934 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_321e5ca99cad37eaa2938d505e255ee4
[17] Punnathanam V, Kotecha P. Yin-Yang-pair Optimization: a novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 2016, 54: 62-79 doi: 10.1016/j.engappai.2016.04.004
[18] Liu M D. Five-elements cycle optimization algorithm for the travelling salesman problem. In: Proceedings of the 18th International Conference on Advanced Robotics (ICAR). Hong Kong, China, 2017.
[19] Liu M D. Five-elements cycle optimization algorithm for solving continuous optimization problems. In: Proceedings of the IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI). Mauritius: IEEE, 2017. 75-79
[20] Yao X, Liu Y, Lin G M. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102 doi: 10.1109/4235.771163
[21] Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 2011, 1(1): 3-18
[22] Yao X, Liu Y. Fast evolution strategies. In: Proceedings of the 6th International Conference on Evolutionary Programming VI. Berlin: Springer, 1997. 151-162
[23] Storn R, Price K. Differential evolution——a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341-359 doi: 10.1023/A:1008202821328
[24] Deb K, Anand A, Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation, 2002, 10(4): 371-395 doi: 10.1162/106365602760972767
[25] He S, Wu Q H, Saunders J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 973-990 doi: 10.1109/TEVC.2009.2011992
[26] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Information Sciences, 2009, 179(13): 2232-2248 doi: 10.1016/j.ins.2009.03.004
[27] Gong W Y, Cai Z H, Ling C X, Li H. A real-coded biogeography-based optimization with mutation. Applied Mathematics and Computation, 2010, 216(9): 2749-2758 doi: 10.1016/j.amc.2010.03.123
[28] Lam A Y S, Li V O K, Yu J J Q. Real-coded chemical reaction optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(3): 339-353 doi: 10.1109/TEVC.2011.2161091
[29] Liang J J, Qu B Y, Suganthan P N, Chen Q. Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization, Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, 2014.
[30] El-Abd M. Cooperative co-evolution using LSHADE with restarts for the CEC15 benchmarks. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada: IEEE, 2016. 4810-4814
[31] Chen L, Peng C D, Liu H L, Xie S L. An improved covariance matrix leaning and searching preference algorithm for solving CEC 2015 benchmark problems. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC). Sendai, Japan: IEEE, 2015. 1041-1045