[1] 段海滨,张祥银,徐春芳. 仿生智能计算[M]. 北京:科学出版社, 2011:11-20. (DUAN H B, ZHANG X Y, XU C F. Bio-inspired Computing[M]. Beijing:Science Press, 2011:11-20.) [2] 张雷,范波.计算智能理论与方法[M].北京:科学出版社,2013:2-9. (ZHANG L, FAN B. Theories and Methods of Computational Intelligence[M]. Beijing:Science Press, 2013:2-9.) [3] 黄竞伟,朱福喜,康立山.计算智能[M].北京:科学出版社,2010:22-29. (HUANG J W, ZHU F X, KANG L S. Computational Intelligence[M]. Beijing:Science Press, 2010:22-29.) [4] 刘勇,马良,张惠珍,等.智能优化算法[M].上海:上海人民出版社,2019:1-9. (LIU Y, MA L, ZHANG H Z, et al. Intelligent Optimization Algorithms[M]. Shanghai:Shanghai People's Publishing House, 2019:1-9.) [5] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1):67-82. [6] HOLLAND J H. Genetic algorithms[J]. Scientific American, 1992, 267(1):66-73. [7] 刘哲,邹涛,孙威,等. 结合实时优化遗传算法的磨削机器人阻抗控制[J]. 控制理论与应用, 2018, 35(12):1788-1795. (LIU Z, ZOU T, SUN W, et al. Impedance control of grinding robot based on real-time optimization genetic algorithm[J]. Control Theory and Applications, 2018, 35(12):1788-1795.) [8] 贾叶玲,董绍华. 基于启发式遗传算法的混合流水车间成套订单问题[J]. 计算机应用, 2019, 39(9):2772-2777. (JIA Y L, DONG S H. Whole-set order problem of hybrid flow shop based on heuristic-genetic algorithms[J]. Journal of Computer Applications, 2019, 39(9):2772-2777.) [9] KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598):671-680. [10] 李元香,项正龙,夏界宁.模拟退火算法的动力系统模型及收敛性分析[J].计算机学报,2019,42(6):1161-1173. (LI Y X, XIANG Z L, XIA J N. Dynamical system models and convergence analysis for simulated annealing algorithm[J]. Chinese Journal of Computers, 2019, 42(6):1161-1173.) [11] 钱龙,黄嵩,倪宣明,等.基于模拟退火算法的知情交易研究[J].系统科学与数学,2019,39(5):703-719. (QIAN L, HUANG S, NI X M, et al. Research of informed trading based on simulated annealing[J]. Journal of Systems Science and Mathematical Sciences, 2019, 39(5):703-719.) [12] 马良.多目标平面选址问题的模拟退火算法[J].系统工程理论与实践,1997,17(3):70-73. (MA L. Simulated annealing algorithm for multi-objective planar location problem[J]. Systems Engineering-Theory and Practice, 1997, 17(3):70-73.) [13] 印会河,张伯讷.中医基础理论[M].上海:上海科学技术出版社,2018:11-15. (YIN H H, ZHANG B N. Basic Theory of Traditional Chinese Medicine[M]. Shanghai:Shanghai Scientific and Technical Publishers, 2018:11-15.) [14] PUNNATHANAM V, KOTECHA P. Yin-Yang-pair optimization:a novel lightweight optimization algorithm[J]. Engineering Applications of Artificial Intelligence, 2016, 54:62-79. [15] HILLS T T, TODD P M, LAZER D, et al. Exploration versus exploitation in space, mind, and society[J]. Trends in Cognitive Sciences, 2015, 19(1):46-54. [16] ALBA E, DORRONSORO B. The exploration/exploitation tradeoff in dynamic cellular genetic algorithms[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(2):126-142. [17] ZHOU Z, ONG Y S, NAIR P B, et al. Combining global and local surrogate models to accelerate evolutionary optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2007, 37(1):66-76. [18] PUNNATHANAM V, KOTECHA P. Reduced Yin-Yang-pair optimization and its performance on the CEC 2016 expensive case[C]//Proceedings of the 2016 Congress on Evolutionary Computation. Piscataway:IEEE, 2016:2996-3002. [19] MAHARANA D, KOMMADATH R, KOTECHA P. Dynamic Yin-Yang pair optimization and its performance on single objective real parameter problems of CEC 2017[C]//Proceedings of the 2017 Congress on Evolutionary Computation. Piscataway:IEEE, 2017:2390-2396. [20] HEIDARI A A, KAZEMIZADE O, HAKIMPOUR F. A new hybrid Yin-Yang-pair-particle swarm optimization algorithm for uncapacitated warehouse location problems[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, XLII-4/W4:373-379. [21] SONG D, LIU J, YANG J, et al. Optimal design of wind turbines on high-altitude sites based on improved Yin-Yang-pair optimization[J]. Energy, 2020, 193:No.116794. [22] RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1):64-79. [23] CAPONETTO R, FORTUNA L, FAZZINO S, et al. Chaotic sequences to improve the performance of evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(3):289-304. [24] RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA:a gravitational search algorithm[J]. Information Sciences, 2009, 179(13):2232-2248. [25] YANG X S. Nature-Inspired Metaheuristic Algorithms[M]. 2nd ed. Beckington:Luniver Press, 2010:21-39. |