Bionic Computing in Higher Organisms from the Perspective of Collective Intelligence: Problem Analysis and Comprehensive Review
XIAO Renbin1, WU Bowen1, ZHAO Jia2, CHEN Zhizhen3
1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; 2. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; 3. Business School, University of Greenwich, London SE10 9LS, UK
Abstract：Focusing on higher organisms, this paper analyzes and develops a comprehensive review of the problems in bionic computing and also proposes and expounds some new views and insights, from the perspective of collective intelligence as a whole, which includes swarm intelligence and crowd intelligence. On the basis of an overview on the research progress of bionic computation in higher organisms (including fundamental higher organisms, regular higher organisms and quasi-man organisms), the reflux phenomenon in the research on the trend of making algorithms marked by “zoo algorithm” in swarm intelligence optimization is found. A reasonable interpretation of the reasons for the formation of the trend of making algorithms from both the bionic-computational dimension and the problem-method dimension. Furthermore, the overall idea of problem solving is given, and the two main development directions of bionic computing for collective intelligence are refined and formed. Emphasis on the expansion of bionic behavior towards cooperative behavior is dominant in the direction of collective intelligence bionic computing development. Aiming at the difficulties existing in the research of swarm intelligence optimization, five bottlenecks that need to be focused on to achieve breakthroughs are proposed. Based on the overall view of “metaphorical bionic computing-normative bionic computing-complex bionic computing”, the new paradigm of intelligent computing of complex bionic computing is advocated, which can guide the direction for higher organism bionic computing.
[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.
