[1] 肖人彬. 群集智能特性分析及其对复杂系统研究的意义[J]. 复杂系统与复杂性科学, 2006, 3(3): 10-19.
XIAO R B. Analysis of characteristics of swarm intelligence and its significance to the research of complex systems[J]. Complex Systems and Complexity Science, 2006, 3(3): 10-19.
[2] 丁璟韬, 徐丰力, 孙浩, 等. 人工智能驱动的复杂系统研究前沿[J]. 电子科技大学学报, 2024, 53(3): 455-461.
DING J T, XU F L, SUN H, et al. Advancements in artificial intelligence-driven complex systems research [J]. Journal of University of Electronic Science and Technology of China, 2024, 53(3): 455-461.
[3] 肖人彬, 陈峙臻. 从群智能优化到群智能进化[J]. 南昌工程学院学报, 2023, 42(1): 1-10.
XIAO R B, CHEN Z Z. From swarm intelligence optimization to swarm intelligence evolution[J]. Journal of Nanchang Institute of Technology, 2023, 42(1): 1-10.
[4] 肖人彬, 李贵, 陈峙臻. 进化超多目标优化研究进展及展望[J]. 控制与决策, 2023, 38(7): 1761-1788.
XIAO R B, Li G, CHEN Z Z. Research progress and prospect of evolutionary many-objective optimization[J]. Control and Decision, 2023, 38(7): 1761-1788.
[5] MARTI R, SEVAUX M, SORENSEN K. 50 years of metaheuristics[J]. European Journal of Operational Research, doi: https://doi.org/10.1016/j.ejor.2024.04.004.
[6] 肖莘玥, 陈峙臻. 资源分配视角下投资组合优化问题的背包模型及其求解[J]. 南昌工程学院学报, 2024, 43(6): 91-98.
XIAO X Y, CHEN Z Z. Knapsack model and its solution for portfolio optimization problem from perspective of resource allocation [J]. Journal of Nanchang Institute of Technology, 2024, 43(6): 91-98.
[7] KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealling[J]. Science, 1983, 220(4598): 671-680.
[8] SHI Y H. An optimization algorithm based on brainstorming process[J]. International Journal of Swarm Intelligence Research, 2011, 2(4): 35-62.
[9] RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems[J]. Computer-Aided Design, 2011, 43(3): 303-315.
[10] LI W, WU W J, WANG H M, et al. Crowd intelligence in AI 2.0 era[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15-43.
[11] 肖人彬, 冯振辉, 王甲海. 群体智能的概念辨析与研究进展及应用分析[J]. 南昌工程学院学报, 2022, 41(1): 1-21.
XIAO R B, FENG Z H, WANG J H. Collective intelligence: conception, research progress and application analyses[J]. Journal of Nanchang Institute of Technology, 2022, 41(1): 1-21.
[12] XIAO R B. Four development stages of collective intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 903-916.
[13] YU C, CHAI Y, LIU Y. Literature review on collective intelligence: a crowd science perspective[J]. International Journal of Crowd Science, 2018, 2(1): 64-73.
[14] MICHELUCCI P, DICKINSON J L. The power of crowds[J]. Science, 2016, 351(6268): 32-33.
[15] AXELROD R, HAMILTON W D. The evolution of cooperation[J]. Science, 1981, 211(4489): 1390-1396.
[16] O’BRYAN L, BEIER M, SALAS E. How approaches to animal swarm intelligence can improve the study of collective intelligence in human teams[J]. Journal of Intelligence, 2020, 8: 9.
[17] 霍春雁. 生物分类阶元名——特殊的专业术语[J]. 中国科技术语, 2023, 25(4): 79-87.
HUO C Y. Biological taxon names: a special category of professional terms[J]. China Terminology, 2023, 25(4): 79-87.
[18] DORIGO M, MANIEZZO V, COLORNI A. Ant systems: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1996, 26(1): 29-41.
[19] POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization[J]. Swarm Intelligence, 2007, 1(1): 33-57.
[20] 李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式:鱼群算法[J]. 系统工程理论与实践, 2002, 22(11): 32-38.
LI X L, SHAO Z J, QIAN J X. An optimizing method based on autonomous animats: fish-swarm algorithm[J]. Systems Engineering-Theory & Practice, 2002, 22(11): 32-38.
[21] 罗伯特·博伊德, 琼·西尔克. 人类的演化[M]. 张鹏, 韩宁, 译. 北京: 商务印书馆, 2021.
[22] PARTRIDGE B L. The structure and function of fish schools[J]. Scientific American, 1982, 246(6): 114-123.
[23] 李晓磊, 钱积新. 基于分解协调的人工鱼群优化算法研究[J]. 电路与系统学报, 2003, 8(1): 1-6.
LI X L, QIAN J X. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques[J]. Journal of Circuits and Systems, 2003, 8(1): 1-6.
[24] 王培崇. 人工鱼群算法研究综述[J]. 中国民航飞行学院学报, 2013, 24(4): 22-26.
WANG P C. Overview of artificial fish swarm algorithm[J]. Journal of Civil Aviation Flight University of China, 2013, 24(4): 22-26.
[25] KOUREPINIS V, ILIOPOULOU C, TASSOPOULOS I, et al. An artificial fish swarm optimization algorithm for the urban transit routine problem[J]. Applied Soft Computing, 2024, 155: 111446.
[26] ZHAO W, WANG L, ZHANG Z, et al. Electric eel foraging optimization: a new bio-inspired optimizer for engineering applications[J]. Expert Systems with Applications, 2023, 238: 122200.
[27] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[28] BIYANTO T R, IRAWAN S, FEBRIANTO H Y, et al. Killer whale algorithm: an algorithm inspired by the life of killer whale[J]. Procedia Computer Science, 2017, 124: 151-157.
[29] RAJAKUMAR B R. The lion’s algorithm: a new nature-inspired search algorithm[J]. Procedia Technology, 2012, 6: 126-135.
[30] WANG B, JIN X P, CHENG B. Lion pride optimizer: an optimization algorithm inspired by lion pride behavior[J]. Science China Information Sciences, 2012, 55: 2369-2389.
[31] YAZDANI M, JOLAI F. Lion optimization algorithm: a nature-inspired metaheuristic algorithm[J]. Journal of Computational Design and Engineering, 2016, 3(1): 24-36.
[32] MIRJALILI, S, SEYED M M, Andrew L. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[33] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法——狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430-2438.
WU H S, ZHANG F M, WU L S. New swarm intelligence algorithm——wolf pack algorithm[J]. Systems Engineering and Electronics, 2013, 35(11): 2430-2438.
[34] PIEREZAN J, COELHO L D S. Coyote optimization algorithm: a new metaheuristic for global optimization problems[C]. 2018 IEEE Congress on Evolutionary Computation (CEC), 2018: 1-8.
[35] KHISHE M, MOSAVI M R. Chimp optimization algorithm[J]. Expert Systems with Applications, 2020, 149: 113338.
[36] ABDOLLAHZADEH B, SOLEIMANIAN G F, MIRJALILI S. Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems[J]. International Journal of Intelligent Systems, 2021, 36(10): 5887-5958.
[37] DAS A K, PRATIHAR D K. Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems[J]. Applied Intelligence, 2022, 52(3): 2942-2974.
[38] MIRJALILI S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems[J]. Neural Computing & Applications, 2016, 27(4): 1053-1073.
[39] DHIMAN G, KUMAR V. Emperor penguin optimizer: a bio-inspired algorithm for engineering problems[J]. Knowledge-Based Systems. 2018, 159: 20-50.
[40] HASHIM F A, HUSSIEN A G. Snake optimizer: a novel meta-heuristic optimization algorithm[J]. Knowledge-Based Systems, 2022, 242: 108320.
[41] CHOPRA N, ANSARI M M. Golden jackal optimization: a novel nature-inspired optimizer for engineering applications[J]. Expert Systems with Applications, 2022, 198: 116924.
[42] DEHGHANI M, MONTAZERI Z, TROJOVSKÁ E, et al. Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2023, 259: 110011.
[43] XUE J, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. Journal of Supercomputing, 2023, 79: 7305-7336.
[44] JAIN M, SINGH V, RANI A. A novel nature-inspired algorithm for optimization: squirrel search algorithm[J]. Swarm and Evolutionary Computation, 2019, 44: 148-175.
[45] FERAHTIA S, HOUARJ A, REZK H et al. Red-tailed hawk algorithm for numerical optimization and real-world problems[J]. Scientific Reports, 2023, 13: 12950.
[46] ABDEL-BASSET M, MOHAMED R, ABOUHAWWASH M. Crested porcupine optimizer: a new nature-inspired metaheuristic[J]. Knowledge-Based Systems, 2024, 284: 111257.
[47] AMIRI M H, HASHJIN N M, MONTAZERI M, et al. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm[J]. Scientific Reports, 2024, 14: 5032.
[48] AL-BETAR M A, AWADALLAH M A, BRAIK M S, et al. Elk herd optimizer: a novel nature-inspired metaheuristic algorithm [J]. Artificial Intelligence Review, 2024, 57(3): 48.
[49] PERAZA-VÁZQUEZ H, PEÑA-DELGADO A, MERINO-TREVIÑO M, et al. A novel metaheuristic inspired by horned lizard defense tactics[J]. Artificial Intelligence Review, 2024, 57(3): 59.
[50] FU Y, LIU D, CHEN J et al. Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems[J]. Artificial Intelligence Review, 2024, 57(5): 123.
[51] FU S, LI K, HUANG H, et al. Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems[J]. Artificial Intelligence Review, 2024, 57(6): 134.
[52] WANG W, TIAN W, XU D, et al. Arctic puffin optimization: a bio-inspired metaheuristic algorithm for solving engineering design optimization[J]. Advances in Engineering Software, 2024, 195: 103694.
[53] ZHANG H, SAN H, CHEN J, et al. Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems[J]. Cluster Computing, 2024,27:12361-12393.
[54] FALAHAH I A, AL-BAIK O, ALOMARJ S, et al. Frilled lizard optimization: a novel bio-inspired optimizer for solving engineering applications[J]. Computers, Materials & Continua, 2024, 79(3): 3631-3678.
[55] BAI J, NGUYEN-XUAN H, ATROSHCHENKO E, et al. Blood-sucking leech optimizer[J]. Advances in Engineering Software, 2024, 195: 103696.
[56] MATURANA D, FOUHEY D. A spetral approach to ghost detection[DB/OL].[2024-10-20].https://www.oneweirdkerneltrick.com/spectral.pdf.
[57] BONABEAU E, DORIGO M, THERAULAZ G. Inspiration for optimization from social insect behaviour[J]. Nature, 2000, 406(6791): 39-42.
[58] 尚玉昌. 动物行为学[M]. 2版. 北京: 北京大学出版社, 2014.
[59] ZHAO R Q, TANG W S. Monkey algorithm for global numerical optimization[J]. Journal of Uncertain Systems, 2008, 2(3): 164-175.
[60] BANSAL J C, SHARMA H, JADON S S, et al. Spider monkey optimization algorithm for numerical optimization[J]. Memetic Computing, 2014, 6(1): 31-47.
[61] 肖人彬, 等. 面向复杂系统的群集智能[M]. 北京: 科学出版社, 2013.
[62] 吴虎胜, 肖人彬. 群智能新研究:角色-匹配的狼群劳动分工[J]. 智能系统学报, 2021, 16(1): 125-133.
WU H S, XIAO R B. A new approach to swarm intelligence: role-matching labor division of a wolfpack[J]. CAAI Transactions on Intelligent Systems, 2021, 16(1): 125-133.
[63] 肖人彬, 王英聪. 一种面向时间分配问题的群智能劳动分工新方法[J]. 智能系统学报, 2019, 14(3): 438-448.
XIAO R B, WANG Y C. A new approach to labor division in swarm intelligence for time allocationproblem[J]. CAAI Transactions on Intelligent Systems, 2019, 14(3): 438-448.
[64] SUCHAK M, EPPLEY T M, CAMPBELL M W, et al. How chimpanzees cooperate in a competitive world[J]. Proceedings of the National Academy of Sciences, 2016, 113(36): 10215-10210.
[65] 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.
[66] JIA H, LU C. Guided learning strategy: a novel update mechanism for metaheuristic algorithms design and improvement[J]. Knowledge-Based Systems, 2024, 286: 111402.
[67] 徐华, 袁源. 智能演化优化[M]. 北京: 清华大学出版社, 2024.
[68] WOLPERT D H, Macready W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82.
[69] WANG G G, TAN Y. Improving metaheuristic algorithms with information feedback models[J]. IEEE Transactions on Cybernetics, 2019, 49(2): 542-555.
[70] CAMACHO-VILLALÓN C L, DORIGO M, STÜTZLE T. Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors[J]. International Transactions in Operational Research,2023, 30(6): 2945-2971.
[71] KARSAU I. Decentralized control of construction behavior in paper wasps: an overview of the stigmergy approach[J]. Artificial Life, 1999, 5(2): 117-136.
[72] 迈克尔·托马塞洛. 人类沟通的起源[M]. 蔡雅菁, 译. 北京: 商务印书馆, 2018: 4-5.
