[1]ZHANG Wanpeng,TAN Siyu,GU Xueqiang,et al.Review of intelligent evolution technology for complex systems[J].CAAI transactions on intelligent systems,2026,21(5):1084-1105.[doi:10.11992/tis.202510041]
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Review of intelligent evolution technology for complex systems

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1084-1105 Column: 综述 Public date: 2026-09-05
Title:
Review of intelligent evolution technology for complex systems
Author(s):
ZHANG Wanpeng1;  TAN Siyu2;  GU Xueqiang1;  WANG Yao1;  WANG Diyong2;  ZHU Shasha2
1. College of Intelligent Sciences, National University of Defense Technology, Changsha 410073, China;
2. Hunan Institute of Advanced Technology, Changsha 410205, China
Keywords:
artificial intelligence; complex systems; intelligent evolution; learning optimization feedback; big data decision making; edge intelligence; multi-agent collaboration; adaptive evolution
CLC:
TP18
DOI:
10.11992/tis.202510041
Abstract:
As a large-scale system that continuously integrates and couples a large number of local autonomous systems, the basic idea of intelligent evolution of complex systems is to use intelligent technology to achieve continuous empowerment of the system. The complex system driven by artificial intelligence and supported by big data has achieved multiple rounds of technological iteration and scene updates. This article focuses on complex artificial intelligence systems. Firstly, it analyzes the basic theories of intelligent evolution, refine the concept of intelligent evolution, and propose an evolution technology integration framework centered around the perception layer, computing layer, and optimization layer of complex systems. and summarizes the four key evolution technologies of big data decision-making, edge intelligence, multi-agent collaboration, and adaptive evolution, summarize the crucial supporting role of the above technologies within complex systems and the development and evolution path of these technologies. It also looks forward to the future direction of complex system evolution driven by artificial intelligence. This article can provide theoretical support for the development path of the next generation of artificial intelligence complex systems.
References:
[1] 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 Jingtao, XU Fengli, SUN Hao, 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
[2] SUN Jiuxiang, YU Yanan, XIN Ling. Research on path planning of AGV based on improved ant colony optimization algorithm[C]//2021 33rd Chinese Control and Decision Conference. Kunming: IEEE, 2021: 7567-7572.
[3] SAHOO S, DALEI R K, RATH S K, et al. Comparative performance analysis of particle swarm optimization and artificial bee colony algorithm for optimization of missile gliding trajectory[C]//2022 OITS International Conference on Information Technology. Bhubaneswar: IEEE, 2022: 187-192.
[4] CHIANG K K, YUAN H W. Application of drones and artificial intelligence in flocking birds[C]//2023 Asia-Pacific Microwave Conference. Taipei: IEEE, 2023: 845.
[5] ABDULGHANI A M, ABDULGHANI M M, WALTERS W L, et al. Multi-agent reinforcement learning system using multi-agent proximal policy optimizer algorithm in SMAC environment[C]//2023 Congress in Computer Science, Computer Engineering, & Applied Computing. Las Vegas: IEEE, 2023: 357-360.
[6] VADHER H H, ARYAN A, VAMSHI K, et al. Unveiling the potential of machine learning: harnessing machine learning for enhanced coronary heart disease detection and intervention[C]//2024 4th International Conference on Advancement in Electronics & Communication Engineering. GHAZIABAD: IEEE, 2024: 1073-1078.
[7] SIMANKOV V S, ONISHCHENKO S V, BUCHATSKIY P Y, et al. An approach to the definition of system intelligence in the management of complex systems[C]//2023 XXVI International Conference on Soft Computing and Measurements. Saint Petersburg: IEEE, 2023: 159-162.
[8] TANG Yiming, LI Lin, LIU Xiaoping. State-of-the-art development of complex systems and their simulation methods[J]. Complex system modeling and simulation, 2021, 1(4): 271-290
[9] LIU Jianran, JI Wen. Crowd intelligence evolution based on complex network[J]. International journal of crowd science, 2021, 5(3): 281-292
[10] TSAREV D, AVDYUSHINA A, ALODJANTS A. Opinion formation and social impact in networks with natural and artificial intelligence systems[C]//2023 16th International Conference Management of Large-Scale System Development. Moscow: IEEE, 2023: 1-5.
[11] GE Jianjun, TANG Siqi, LI Mingqiang, et al. Information theory and construction methods of complex perception systems[J]. Journal of radars, 2025, 14(3): 651-663
[12] 钱学森, 于景元, 戴汝为. 一个科学新领域: 开放的复杂巨系统及其方法论[J]. 自然杂志, 1990(1): 3-10, 64 QIAN Xuesen, YU Jingyuan, DAI Ruwei. A new scientific field—open complex giant systems and its methodology[J]. Nature journal, 1990(1): 3-10, 64
[13] SATORNINO C B, DU Shuili, GREWAL D. Using artificial intelligence to advance sustainable development in industrial markets: a complex adaptive systems perspective[J]. Industrial marketing management, 2024, 116: 145-157
[14] TSIEN H S. Engineering Cybernetics[M]. New York; London: McGraw-Hill, 1954.
[15] WEAVER W. Science and complexity[J]. American scientist, 1948, 36(4): 536-544
[16] ROSENBLUETH A, WIENER N. The role of models in science[J]. Philosophy of science, 1945, 12(4): 316-321
[17] PRIGOGINE I. Time, structure, and fluctuations[J]. Science, 1978, 201(4358): 777-785
[18] PANDEY M, KUNDA N S S, KC P, et al. A reinforcement approach for fleet optimization in multi-agent autonomous cars[C]//2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies. Trichirappalli: IEEE, 2024: 1-5.
[19] ANABEZA C C, BANDALA A A. Fuzzy logic controller for autonomous environment-based drone swarm formation[C]//2023 IEEE Region 10 Symposium. Canberra: IEEE, 2023: 1-5.
[20] SUN Shaobin, HUANG Hao, WANG Wendi, et al. Distributed energy storage with peak shaving and voltage regulation considerations[C]//2024 IEEE PES 16th Asia-Pacific Power and Energy Engineering Conference. Nanjing: IEEE, 2024: 1-5.
[21] BEZUMNOV D N, VORONOV V I. Development of the research stand for exploration of models and algorithms for group control of ground-based mobile robots[C]//2021 Systems of Signals Generating and Processing in the Field of on Board Communications. Moscow: IEEE, 2021: 1-6.
[22] YU Youshan, LAKEMOND N, HOLMBERG G. AI in the context of complex intelligent systems: engineering management consequences[J]. IEEE transactions on engineering management, 2024, 71: 6512-6525
[23] WANG J, WANG S, FONG M C, et al. What can complex systems theory tell us about understanding in the human–AI communication system? [C]//2024 6th International Conference on Natural Language Processing. Xi’an: IEEE, 2024: 650-656.
[24] ALDOIHI S, ALBLALAIHID K, ALZEMAIA F, et al. The role of system modeling on artificial intelligence: a review of emerging trends[C]//2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering. Milano: IEEE, 2023: 161-165.
[25] MA Jun, SUN Qian. Research on intelligent adaptive optimization model system of computing system based on reinforcement learning[C]//2024 IEEE 4th International Conference on Data Science and Computer Application. Dalian: IEEE, 2024: 882-887.
[26] DODIG-CRNKOVIC G. The evolution of intelligence from active matter to complex intelligent systems via agent-based autopoiesis[J]. Proceedings, 2025, 126(1): 2
[27] ESHETU F B, BARNES I, NAHRUNG H F, et al. Unexpected diversity in historical biological control programs: population genetics of the nematode deladenus siricidicola in Australia and New Zealand[J]. Biological control, 2023, 180: 105183
[28] XUE Min, WANG Xiaojing, FU Chao. CD-DERDM: Data-driven evidential reasoning decision-making method with concept drift[J]. Computers & industrial engineering, 2025, 209: 111458
[29] 何冠岐, 叶茗媛. 涌现系统论: 对人工智能 “灵性” 的呼唤[J]. 系统科学学报, 2025, 33(2): 58-63 HE Guanqi, YE Mingyuan. Emergent systems theory: call for “spirituality” of artificial intelligence[J]. Chinese journal of systems science, 2025, 33(2): 58-63
[30] LEE H, JIANG Ming, ZHAO Qi. FedAssist: federated learning in AI-powered prosthetics for sustainable and collaborative learning[C]//2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando: IEEE, 2024: 1-5.
[31] PU Xingcheng, YANG Hua, PENG Chunlian, et al. Group consensus of heterogeneous multi-agent systems with leader and time delay in switching networks based on delta operator method[C]//2021 33rd Chinese Control and Decision Conference. Kunming: IEEE, 2021: 2162-2166.
[32] YANG Aihui, LIN Zhiyao, LIN Ke, et al. Optimal energy scheduling of a microgrid based on offline-to-online deep reinforcement learning[C]//2024 6th International Conference on Energy Systems and Electrical Power. Wuhan: IEEE, 2024: 1088-1092.
[33] L? Yali, DUAN Jingpu, LI Xiong. A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks[J]. Computer science review, 2024, 52: 100635
[34] BAR-YAM Y. From big data to important information[J]. Complexity, 2016, 21(S2): 73-98
[35] 蒋良孝, 蔡之华, 刘钊. 一种基于粗糙集的决策规则挖掘算法[J]. 微型机与应用, 2004, 23(3): 7-9 JIANG Liangxiao, CAI Zhihua, LIU Zhao. an mining algorithm of decision rules based on rough sets[J]. Microcomputer & its applications, 2004, 23(3): 7-9
[36] QIAN Jin, L? Ping, YUE Xiaodong, et al. Hierarchical attribute reduction algorithms for big data using MapReduce[J]. Knowledge-based systems, 2015, 73: 18-31
[37] LI Shaoyong, LI Tianrui, ZHANG Zhixue, et al. Parallel computing of approximations in dominance-based rough sets approach[J]. Knowledge-based systems, 2015, 87: 102-111
[38] ABDEL-BASSET M, MOHAMED M. RETRACTED: the role of single valued neutrosophic sets and rough sets in smart city: imperfect and incomplete information systems[J]. Measurement, 2018, 124: 47-55
[39] BANERJEE S, BADR Y. Evaluating decision analytics from mobile big data using rough set based ant colony[M]//Mobile Big Data: A Roadmap from Models to Technologies. Cham: Springer, 2018: 217-231.
[40] HU Qinghua, ZHANG Lingjun, ZHOU Yucan, et al. Large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets[J]. IEEE transactions on fuzzy systems, 2018, 26(1): 226-238
[41] BONCZEK R H, HOLSAPPLE C W, WHINSTON A B. The evolving roles of models in decision support systems[J]. Decision sciences, 1980, 11(2): 337-356
[42] TANG Mingyang, WU Yafeng, WANG J. An intelligent decision system for debugging engine fuel regulators[J]. Expert systems with applications, 2025, 265: 125987
[43] JAMUNA R. ID3 algorithm in data mining applied to diabetes database[J]. International journal in IT engineering, 2015, 3(3): 10-28
[44] LIN K W, ZHENG Yajun, CHEN J C, et al. A parallel and distributed C4.5 algorithm in cloud computing environments[J]. Computing, 2025, 107(2): 68
[45] WANG Yanzhi, WANG Jianxiao, ZHANG Haoran, et al. Bridging prediction and decision: Advances and challenges in data-driven optimization[J]. Nexus, 2025, 2(1): 100057
[46] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[C]//International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2013: 1-9.
[47] BOJARSKI M, DEL TESTA D, DWORAKOWSKI D, et al. End to end learning for self-driving cars[EB/OL]. (2016-04-25)[2025-10-30]. https://arxiv.org/abs/1604.07316.
[48] ZOPH B, LE Q V. Neural architecture search with reinforcement learning[C]//Proceedings of the International Conference on Learning Representations. Toulon: OpenReview. net, 2017: 1-10.
[49] 曾康铭, 吴杏. 多层概率决策的网络大数据协作融合算法[J]. 电子技术应用, 2018, 44(6): 133-137 ZENG Kangming, WU Xing. The network big data cooperative fusion algorithm based on multi layer probabilistic joint decision[J]. Application of electronic technique, 2018, 44(6): 133-137
[50] HUANG Zhiren, WANG Pu, ZHANG Fan, et al. A mobility network approach to identify and anticipate large crowd gatherings[J]. Transportation Research Part B: Methodological, 2018, 114: 147-170
[51] ZHAN Guojian, ZHENG Ziang, LI S E. Canonical form of datatic description in control systems[C]//2025 American Control Conference. Denver: IEEE, 2025: 3479-3484.
[52] BUKHAROV O E, BOGOL?BOV D P. Development of a decision support system based on neural networks and a genetic algorithm[J]. Expert systems with applications, 2015, 42(15/16): 6177-6183
[53] YU Hong, ZHOU Qingfeng, LIU Man. A dynamic composite web services selection method with QoS-aware based on AND/OR graph[J]. International journal of computational intelligence systems, 2014, 7(4): 660
[54] WANG Zhenpo, SUN Zhenyu, LIU Peng, et al. Intelligent decision support platform of new energy vehicles[J]. Journal of systems engineering and electronics, 2022, 33(4): 785-791
[55] DOSHI A R, BELL J J, MIRZAYEV E, et al. Generative artificial intelligence and evaluating strategic decisions[J]. Strategic management journal, 2025, 46(3): 583-610
[56] GOMEZ C, CHO S M, KE Shichang, et al. Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review[J]. Frontiers in computer science, 2025, 6: 1521066
[57] LEE M H, TOK M Z Y. Towards uncertainty aware task delegation and human-AI collaborative decision-making[C]//Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. Athens: ACM, 2025: 2274-2289.
[58] WANG Ye, XU Hansong, HUA Kun, et al. P3FL: a privacy-preserving personalized federated learning framework for collaborative smart home predictions and decision-making[J]. IEEE internet of things journal, 2025, 12(15): 29948-29960
[59] GENG Wei, XIAO Baidi, LI Rongpeng, et al. Noise distribution decomposition based multi-agent distributional reinforcement learning[J]. IEEE transactions on mobile computing, 2025, 24(3): 2301-2314
[60] CHEN Jiteng, ZHANG Zehui, FAN Dan, et al. Distributed decision making for electromagnetic radiation source localization using multi-agent deep reinforcement learning[J]. Drones, 2025, 9(3): 216
[61] JIAO Yongkang, FU Wenxing, CAO Xinying, et al. Collaborative decision-making for UAV swarm confrontation based on reinforcement learning[J]. IET control theory & applications, 2025, 19: e12781
[62] GARG D, CHLI M, VOGIATZIS G. Multi-agent deep reinforcement learning for traffic optimization through multiple road intersections using live camera feed[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes: IEEE, 2020: 1-8.
[63] BAGGIO G, BASSETT D S, PASQUALETTI F. Data-driven control of complex networks[J]. Nature communications, 2021, 12: 1429
[64] BOYAC? T, CANYAKMAZ C, DE V?RICOURT F. Human and machine: the impact of machine input on decision making under cognitive limitations[J]. Management science, 2023, 70(2): 1258-1275
[65] SATYANARAYANAN M, BAHL P, CACERES R, et al. The case for VM-based cloudlets in mobile computing[J]. IEEE pervasive computing, 2009, 8(4): 14-23
[66] HAN Song, MAO Huizi, DALLY W J. Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding[C]//International Conference on Learning Representations. San Juan: OpenReview.net, 2016: 1-14.
[67] HOWARD A G, ZHU Menglong, CHEN Bo, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17)[2025-11-19]. https://doi.org/10.48550/arXiv.1704.04861.
[68] ZHANG Xiangyu, ZHOU Xinyu, LIN Mengxiao, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6848-6856.
[69] IANDOLA F N, HAN Song, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and model size[EB/OL]. (2016-11-04)[2025-11-19]. https://doi.org/10.48550/arXiv.1602.07360.
[70] HAN Kai, WANG Yunhe, TIAN Qi, et al. GhostNet: more features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 1577-1586.
[71] TAN Mingxing, CHEN Bo, PANG Ruoming, et al. MnasNet: platform-aware neural architecture search for mobile[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 2815-2823.
[72] TAN Mingxing, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning. Los Angeles: PMLR, 2019: 1-11.
[73] CAI Han, ZHU Ligeng, HAN Song. ProxylessNAS: direct neural architecture search on target task and hardware[C]//International Conference on Learning Representations. New Orleans: OpenReview.net, 2019: 1-13.
[74] JIAO Xiaoqi, YIN Yichun, SHANG Lifeng, et al. TinyBERT: distilling BERT for natural language understanding[C]//Findings of the Association for Computational Linguistics: EMNLP 2020. Online: Association for Computational Linguistics, 2020: 4163-4174.
[75] MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale: PMLR, 2017: 1273-1282.
[76] YIN Benshun, CHEN Zhiyong, TAO Meixia. Predictive GAN-powered multi-objective optimization for hybrid federated split learning[J]. IEEE transactions on communications, 2023, 71(8): 4544-4560
[77] LENG Kai, ZHANG Zhijie, LIU Jie, et al. SuperEdge: towards a generalization model for self-supervised edge detection[EB/OL]. (2024-01-04)[2025-11-19]. https://doi.org/10.48550/arXiv.2401.02313.
[78] WANG Wei, ZHOU Shibo, LI Jingxi, et al. Temporal pulses driven spiking neural network for time and power efficient object recognition in autonomous driving[C]//2020 25th International Conference on Pattern Recognition. Milan: IEEE, 2021: 6359-6366.
[79] ZHANG Guanglin, NI Sifan, ZHAO Ping. Learning-based joint optimization of energy delay and privacy in multiple-user edge-cloud collaboration MEC systems[J]. IEEE Internet of Things journal, 2022, 9(2): 1491-1502
[80] SUN Zemin, SUN Geng, WU Qingqing, et al. TJCCT: a two-timescale approach for UAV-assisted mobile edge computing[J]. IEEE transactions on mobile computing, 2025, 24(4): 3130-3147
[81] LUO Haoxiang, LIU Yinqiu, ZHANG Ruichen, et al. Toward edge general intelligence with multiple-large language model (multi-LLM): architecture, trust, and orchestration[J]. IEEE transactions on cognitive communications and networking, 2025, 11(6): 3563-3585
[82] MEHTA S, RASTEGARI M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer[EB/OL]. (2022-03-04)[2025-11-19]. https://doi.org/10.48550/arXiv.2110.02178.
[83] ANASOSALU VASU P K, GABRIEL J, ZHU J, et al. FastViT: a fast hybrid vision transformer using structural reparameterization[C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 5762-5772.
[84] LI Yanyu, HU Ju, WEN Yang, et al. Rethinking vision transformers for MobileNet size and speed[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 16843-16854.
[85] WANG Ao, CHEN Hui, LIN Zijia, et al. Rep ViT: revisiting mobile CNN from ViT perspective[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 15909-15920.
[86] SETYAWAN N, SUN C C, HSU M H, et al. MicroViT: a vision transformer with low complexity self attention for edge device[C]//2025 IEEE International Symposium on Circuits and Systems. London: IEEE, 2025: 1-5.
[87] MOR N A, CANALS V, GALAN-PRADO F, et al. Hardware-optimized reservoir computing system for edge intelligence applications[J]. Cognitive computation, 2023, 15(5): 1461-1469
[88] LIN Weiwei, LIN Jinhui, ZHANG Haotong, et al. Cacomp: a cloud-assisted collaborative deep learning compiler framework for DNN tasks on edge[J]. IEEE transactions on computers, 2025, 74(8): 2663-2674
[89] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[EB/OL]. (2013-12-19)[2025-11-19]. https://doi.org/10.48550/arXiv.1312.5602.
[90] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529-533
[91] LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[EB/OL]. (2019-07-05)[2025-11-19]. https://doi.org/10.48550/arXiv.1509.02971.
[92] SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[EB/OL]. (2017-08-28)[2025-11-19]. https://doi.org/10.48550/arXiv.1707.06347.
[93] WANG Zuoxun, CUI Chuanyu, SUI Jinxue, et al. Improved independent Q-learning for temperature monitoring of fiber-optic temperature sensor arrays based on chaotic pulsed intense light controller and nonlinear estimation of equilibrium system[J]. IEEE sensors journal, 2025, 25(2): 3388-3403
[94] LOWE R, WU Yi, TAMAR A, et al. Multi-agent actor-critic for mixed cooperative-competitive environments[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM, 2017: 6382-6393.
[95] JAKOB NF, GREGORY F, TRIANTAFYLLOS A, et al. Counterfactual multi-agent policy gradients[C]. Proceedings of the AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017: 2974-2982.
[96] LIU Yong, WANG Weixun, HU Yujing, et al. Multi-agent game abstraction via graph attention neural network[C]//Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020: 7211-7218.
[97] YU Chao, VELU A, VINITSKY E, et al. The surprising effectiveness of PPO in cooperative multi-agent games[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans: ACM, 2022: 24611-24624.
[98] CHEN Lili, LU K, RAJESWARAN A, et al. Decision transformer: reinforcement learning via sequence modeling[EB/OL]. (2021-06-24)[2025-11-19]. https://doi.org/10.48550/arXiv.2106.01345.
[99] SCOTT R, KONRAD Z, EMILIO P, et al. A generalist agent[EB/OL]. (2022-05-12)[2025-11-19]. https://arxiv.org/abs/2205.06175.
[100] LI Yiming, MA Dekun, AN Ziyan, et al. V2X-sim: multi-agent collaborative perception dataset and benchmark for autonomous driving[J]. IEEE robotics and automation letters, 2022, 7(4): 10914-10921
[101] 彭俊宇, 张华缨, 高天, 等. 基于CTDE的多智能体的决策方法及装置: 202411925552[P]. 2025-05-27. PENG Junyu, ZHANG Huaying, GAO Tian, et al. Decision making method and device for multi-agent based on CDTE: 202411925552[P]. 2025-05-27.
[102] LI Guohao, ABED AL KADER HAMMOUD H, ITANI H, et al. CAMEL: communicative agents for “mind” exploration of large language model society[EB/OL]. (2023-11-02)[2025-11-19]. https://doi.org/10.48550/arXiv.2303.17760.
[103] ZHOU Xueyang, WANG Weidong, LU Lin, et al. Automating safety enhancement for LLM-based agents with synthetic risk scenarios[EB/OL]. (2025-05-23)[2025-11-19]. https://arxiv.org/abs/2505.17735v1.
[104] KANNAN S S, VENKATESH V L N, MIN B C. SMART-LLM: smart multi-agent robot task planning using large language models[C]//2024 IEEE/RSJ International Conference on Intelligent Robots and Systems. Abu Dhabi: IEEE, 2024: 12140-12147.
[105] LIU Genjia, HU Yue, XU Chenxin, et al. Toward collaborative autonomous driving: simulation platform and end-to-end system[J]. IEEE transactions on pattern analysis and machine intelligence, 2025, 47(8): 6566-6584
[106] CAI Zikui, SHABIHI S, AN Bang, et al. AegisLLM: scaling agentic systems for self-reflective defense in LLM security[EB/OL]. (2025-06-13)[2025-11-19]. https://doi.org/10.48550/arXiv.2504.20965.
[107] ZHANG Songge, CHENG Guoliang, LI Zuguang, et al. SplitLLM: hierarchical split learning for large language model over wireless network[EB/OL]. (2025-01-23)[2025-11-19]. https://doi.org/10.48550/arXiv.2501.13318.
[108] 冯雯, 姜少杰. 一种基于大语言模型的自适应拥塞控制算法及系统: 202510937925[P]. 2025-08-05. FENG Wen, JIANG Shaojie. An adaptive congestion control algorithm and system based on large language models: 202510937925[P]. 2025-08-05.
[109] TANG Jian, ZHENG Wenming, YAN Fangfang, et al. APSCC: adaptive congestion control for packet-sprayed RDMA networks in AI clusters[C]//2025 IEEE International Conference on High Performance Computing and Communications. Exeter: IEEE, 2025: 584-591.
[110] WANG Jianhao, REN Zhizhou, LIU T, et al. QPLEX: duplex dueling multi-agent Q-learning[EB/OL]. (2021-10-04)[2025-11-19]. https://doi.org/10.48550/arXiv.2008.01062.
[111] CAO Huazi, SHEN Jiahao, ZHANG Yin, et al. Proximal cooperative aerial manipulation with vertically stacked drones[J]. Nature, 2025, 646(8085): 576-583
[112] HOLLAND J H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology[M]. Ann Arbor: The University of Michigan Press, 1975.
[113] KAUFFMAN S, LEVIN S. Towards a general theory of adaptive walks on rugged landscapes[J]. Journal of theoretical biology, 1987, 128(1): 11-45
[114] STORN R, PRICE K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997, 11(4): 341-359
[115] JIN Honglin, WANG Xueping, CHENG Shi, et al. Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm[J]. Swarm and evolutionary computation, 2024, 89: 101649
[116] JIA Yunjian, WANG Shankun, LIANG Liang, et al. A flower pollination optimization algorithm based on cosine cross-generation differential evolution[J]. Sensors, 2023, 23(2): 606
[117] PANAGANT N, BUREERAT S. Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution[J]. Engineering optimization, 2018, 50(10): 1645-1661
[118] SHIM V A, TAN K C, TANG Huajin. Adaptive memetic computing for evolutionary multiobjective optimization[J]. IEEE transactions on cybernetics, 2015, 45(4): 610-621
[119] 邢立宁, 罗天羽, 李豪, 等. 面向防空资源分配优化问题的自适应演化算法[J]. 中国科学: 技术科学, 2024, 54(9): 1707-1719 XING Lining, LUO Tianyu, LI Hao, et al. Adaptive evolutionary algorithm for air defense resource allocation optimization[J]. Scientia sinica (technologica), 2024, 54(9): 1707-1719
[120] ZHANG Jingqiao, SANDERSON A C. JADE: adaptive differential evolution with optional external archive[J]. IEEE transactions on evolutionary computation, 2009, 13(5): 945-958
[121] 郭肇禄, 向传娇, 杨火根, 等. 基于双重经验结合的自适应差分进化算法[J]. 华中科技大学学报(自然科学版), 2024, 52(6): 171-178 GUO Zhaolu, XIANG Chuanjiao, YANG Huogen, et al. Adaptive differential evolution algorithm based on dual experience combination[J]. Journal of Huazhong University of Science and Technology (natural science edition), 2024, 52(6): 171-178
[122] DA Bingshui, GUPTA A, ONG Y S. Curbing negative influences online for seamless transfer evolutionary optimization[J]. IEEE transactions on cybernetics, 2019, 49(12): 4365-4378
[123] ZHANG Qingfu, LI Hui. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE transactions on evolutionary computation, 2007, 11(6): 712-731
[124] HAN Mingzhang, FAN Mingjie, ZHAO Xinchao, et al. Knowledge-based hyper-parameter adaptation of multi-stage differential evolution by deep reinforcement learning[J]. Neurocomputing, 2025, 648: 130633
[125] BAI Lu, LIN Wu, GUPTA A, et al. From multitask gradient descent to gradient-free evolutionary multitasking: a proof of faster convergence[J]. IEEE transactions on cybernetics, 2022, 52(8): 8561-8573
[126] JIA Shian, WANG Xinbo, SONG Mingli, et al. Agent centric operating system: a comprehensive review and outlook for operating system[EB/OL]. (2024-11-15)[2025-11-19]. https://doi.org/10.48550/arXiv.2411.17710.
[127] CHRISTEN P. System metamodeling of open-ended evolution implemented with self-modifying code[J]. Complex systems, 2024, 32(4): 353-380
[128] LI Changhe, YANG Rui, ZHOU Li, et al. Adaptive multipopulation evolutionary algorithm for contamination source identification in water distribution systems[J]. Journal of water resources planning and management, 2021, 147(5): 04021014
[129] 聂雷, 陈美君, 吴黎兵. 基于自适应分簇和演化博弈的异构车载网络选择方法[J]. 武汉大学学报(理学版), 2023, 69(2): 169-177 NIE Lei, CHEN Meijun, WU Libing. Adaptive clustering and evolutionary game based network selection method for heterogeneous vehicular networks[J]. Journal of Wuhan University (natural science edition), 2023, 69(2): 169-177
[130] 曲正. 动态演化博弈下电力线通信信道路径带宽自适应分配算法[J]. 电子器件, 2025, 48(1): 50-54 QU Zheng. Adaptive allocation algorithm for path bandwidth of power line communication channels under dynamic evolutionary game[J]. Chinese journal of electron devices, 2025, 48(1): 50-54
[131] 李晚璐, 彭丽娟, 王子威. 面向建筑拆除碳排放的改进自适应演化预测模型设计[J]. 自动化与仪器仪表, 2025(7): 190-194 LI Wanlu, PENG Lijuan, WANG Ziwei. Improved adaptive evolutionary prediction model design for building demolition carbon emission[J]. Automation & instrumentation, 2025(7): 190-194
[132] ZHANG Hebing, ZHENG Xiaojing. Invariable distribution of co-evolutionary complex adaptive systems with agent’s behavior and local topological configuration[J]. Mathematical biosciences and engineering, 2024, 21(2): 3229-3261
[133] LU Dejiang, CHENG Siyi, CHEN You, et al. An adaptive parameter evolutionary marine predators algorithm for joint resource scheduling of cooperative jamming networked radar systems[J]. Remote sensing, 2025, 17(8): 1325
[134] Xiao Renbin. Four development stages of collective intelligence[J]. Frontiers of information technology & electronic engineering, 2024, 25(7): 903-916
[135] 肖人彬, 赵嘉, 崔志华, 等. 群体智能研究的范式转变[J]. 太原科技大学学报, 2025, 46(5): 401-410 XIAO Renbin, ZHAO Jia, CUI Zhihua, et al. Paradigm shift in collective intelligence research[J]. Journal of Taiyuan University of Science and Technology, 2025, 46(5): 401-410
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