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A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 904-916页

作者： Kaizhou Gao Zhiguang Cao Le Zhang Zhenghua Chen Yuyan Han Quanke Pan IEEE the Macao Institute of Systems Engineering at Macao University of Science and Technology 999078 China School of Computer at Liaocheng Univeristy Liaocheng252059China the Department of Industrial Systems Engineering and Management National University of SingaporeSingapore Centre for Maritime Studies National University of SingaporeSingapore the Institute for Infocomm Research(I2R) the Agency for ScienceTechnology and Research(A*STAR)Singapore the School of Computer at Liaocheng Univeristy Liaocheng 252059China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many *** to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving *** than 60%of the publications are related to SI and *** paper intents to give a comprehensive literature review of SI and EA for solving ***,the mathematical model of FJSP is presented and the constraints in applications are ***,the encoding and decoding strategies for connecting the problem and algorithms are *** strategies for initializing algorithms?population and local search operators for improving convergence performance are ***,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are ***,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

关键词： Evolutionary algorithm flexible job shop scheduling review swarm intelligence

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Roadmap on Neuromorphic Photonics

arXiv

引用

arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Results of the Ontology Alignment Evaluation Initiative 2022 17

Results of the Ontology Alignment Evaluation Initiative 2022

引用

17th International Workshop on Ontology Matching, OM 2022

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Buche, Patrice Castro, Leyla J. Chen, Jiaoyan Dong, Hang Fallatah, Omaima Faria, Daniel Fundulaki, Irini Hertling, Sven He, Yuan Horrocks, Ian Huschka, Martin Ibanescu, Liliana Jiménez-Ruiz, Ernesto Karam, Naouel Laadhar, Amir Lambrix, Patrick Li, Huanyu Li, Ying Michel, Franck Nasr, Engy Paulheim, Heiko Pesquita, Catia Saveta, Tzanina Shvaiko, Pavel Trojahn, Cassia Verhey, Chantelle Wu, Mingfang Yaman, Beyza Zamazal, Ondrej Zhou, Lu Linköping University Swedish E-Science Research Center Linköping Sweden Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany UMR IATE INRAE University of Montpellier France ZB MED Information Centre for Life Sciences Germany Department of Computer Science The University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Information School The University of Sheffield Sheffield United Kingdom University of Lisbon Portugal Institute of Computer Science-FORTH Heraklion Greece Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut EMI Germany Université Paris-Saclay INRAE AgroParisTech UMR MIA Paris-Saclay France City University of London United Kingdom Fraunhofer FOKUS Institute for Applied Informatics University of Leipzig Germany University of Stuttgart Germany University of Gävle Sweden University Côte d'Azur CNRS Inria France Albert Ludwig University of Freiburg Germany LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Trentino Digitale SpA Trento Italy Institut de Recherche en Informatique de Toulouse France World Data System International Technology Office United States Australian Research Data Commons Australia ADAPT Centre Trinity College Dublin Ireland Prague University of Economics and Business Czech Republic TigerGraph Inc. United States SIRIUS University of Oslo Norway

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

引用

arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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Parallel Building: A Complex System Approach for Smart Building Energy Management

引用

IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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The Method of Neural Network Control over the Process of Manufacturing Foil Solar Panels 2

The Method of Neural Network Control over the Process of Man...

引用

2nd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2020

作者： Sachenko, Anatoliy Ivakhiv, Orest Vyshnia, Volodymyr Grzeszczyk, Konrad Osolinskyi, Oleksandr Novosad, Stanislav Kochan, Volodymyr Nakonechnyi, Markiyan Kochan, Orest Kopania, Lukasz Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics Radom Poland Lviv Polytechnic National University Department of Intelligent Mechatronics and Robotics Lviv Ukraine Dnipropetrovsk State University of Internal Affairs Department of Economic and Information Security Dnipro Ukraine International Vision Machinery VISORT Sp. J Radom Poland Ternopil National Economic University Department for Information Computer Systems and Control Ternopil Ukraine Ternopil National Economic University Research Institute for Intelligent Computer Systems Ternopil Ukraine Lviv Polytechnic National University Department of Computerized Automation Systems Lviv Ukraine Lviv Polytechnic National University Department of Measuring Information Technologies Lviv Ukraine

ISBN: (纸本)9781728190822

Authors have developed a method of control over one of the main technological processes of manufacturing foil solar panels, i.e., burning tracks in the layers of semiconductor material and insulation. The proposed method is based on neural networks trained directly on the control object. This eliminates the laborious process of identifying the parameters of the control object mathematical model and the impact of its error on the training outcomes. To clarify the conditions for optimizing a certain law for generating the control action by neural networks various laws of control have been simulated. It's established that laws of control can be changed according to the current situation of the control object. Simulation outcomes proofed that the most efficient is the system with the PI- controller. © 2020 IEEE.

关键词： Process control

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An ecologically valid examination of event-based and time-based prospective memory using immersive virtual reality: The effects of delay and task type on everyday prospective memory.

TechRxiv

引用

TechRxiv 2021年

作者： Kourtesis, Panagiotis Collina, Simona Doumas, Leonidas A.A. MacPherson, Sarah E. Human Cognitive Neuroscience Department of Psychology University of Edinburgh Edinburgh United Kingdom Department of Psychology University of Edinburgh Edinburgh United Kingdom Lab of Experimental Psychology Suor Orsola Benincasa University of Naples Naples Italy Interdepartmental Centre for Planning and Research "Scienza Nuova" Suor Orsola Benincasa University of Naples Naples Italy National Research Institute of Computer Science and Automation INRIA Rennes France Univ Rennes Rennes France Research Institute of Computer Science and Random Systems IRISA Rennes France French National Centre for Scientific Research CNRS Rennes France

Recent research has focused on assessing either event- or time-based prospective memory (PM) using laboratory tasks. Yet, the findings pertaining to PM performance on laboratory tasks are often inconsistent with the findings on corresponding naturalistic experiments. Ecologically valid neuropsychological tasks resemble the complexity and cognitive demands of everyday tasks, offer an adequate level of experimental control, and allow a generalisation of the findings to everyday performance. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality neuropsychological battery with enhanced ecological validity, was implemented to comprehensively assess everyday PM (i.e., focal and non-focal event-based, and time-based). The effects of the length of delay between encoding and initiating the PM intention and the type of PM task on everyday PM performance were examined. The results revealed that everyday PM performance was affected by the length of delay rather than the type of PM task. The effect of the length of delay differentially affected performance on the focal, nonfocal, and time-based tasks and was proportional to the PM cue focality (i.e., semantic relationship with the intended action). This study also highlighted methodological considerations such as the differentiation between functioning and ability, distinction of cue attributes, and the necessity of ecological validity. © 2021, CC BY.

关键词： Virtual reality

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Innovative dust detection and efficient cleaning of PV Panels: A CNN‑RF approach using I–V curve data transformed into RGB mosaics

引用

Energy Conversion and Management: X 2025年 27卷

作者： Safia Babikir Bashir Mena Maurice Farag Abdul-Kadir Hamid Ali A. Adam Ramesh C. Bansal Nsilulu T Mbungu A. Elnady Ahmed G. Abo-Khalil Mousa Hussein Smart Grid Research Group Sustainable Energy and Power Systems Research Center Research Institute of Sciences and Engineering (RISE) Department of Electrical Engineering University of Sharjah Sharjah 27272 United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah 27272 Sharjah United Arab Emirates Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Department of Electrical and Communication Engineering United Arab Emirates University Al Ain United Arab Emirates

Photovoltaic (PV) panels are vital for renewable energy generation, yet their efficiency is critically hindered by environmental challenges such as dust accumulation, especially in arid regions like the UAE. Dust buildup can reduce efficiency by up to 30% within a month, threatening the sustainability of solar power, which is projected to supply 10% of global energy by 2030. Existing cleaning methods are unsustainable, consuming an estimated 10 billion gallons of water annually, enough to meet the drinking needs of 2 million people, necessitating the development of a cost-effective, resource-efficient alternative. This research presents a novel machine learning-based system to automate dust detection and optimize cleaning, significantly reducing water consumption while improving power generation efficiency. The methodology transforms I-V curve electrical parameters into RGB mosaic images, enabling precise classification of operational states such as normal operation, dust accumulation, shading, and faults without relying on external imaging devices. The system is built on a hybrid model combining Convolutional Neural Networks (CNN) and Random Forest (RF) classifiers (CNN-RF), where the CNN extracts high-level features from RGB mosaic images, and the RF classifier accurately categorizes operational states. Upon detecting dust accumulation, a secondary CNN-RF model classifies the severity into low, moderate, or heavy, guiding an optimized cleaning process that minimizes water usage while maintaining cleaning effectiveness. The primary CNN-RF model achieved 100% accuracy in classifying operational states using RGB mosaic images, surpassing the 97% accuracy achieved by I–V curve-based methods. Furthermore, the secondary CNN-RF model for dust severity classification attained an accuracy of 98% using RGB mosaic images, compared to only 68% when using traditional I–V curves, highlighting the superior performance of RGB mosaic images in detecting fine-grained dust levels. T

关键词： Automated Cleaning Convolutional Neural Networks Dust Detection Machine Learning Photovoltaics

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Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks

Driving on Highway by Using Reinforcement Learning with CNN ...

引用

IEEE International Conference on Intelligent Engineering systems (INES)

作者： László Szőke Szilárd Aradi Tamás Bécsi Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics H-1111 Budapest Hungary Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences H-1111 Budapest Hungary

ISBN: (数字)9781728110592

ISBN: (纸本)9781728110608

This work presents a powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. Our goal is to create an agent that is capable of navigating safely in changing highway traffic and successfully accomplish to get through the defined section keeping the reference speed. Meanwhile, creating a state representation that is capable of extracting information from images based on the actual highway situation. The algorithm uses Convolutional Neural Network (CNN) with Long-Short Term Memory (LSTM) layers as a function approximator for the agent with discrete action space on the control level, e.g., acceleration and lane change. Simulation of Urban MObility (SUMO), an open-source microscopic traffic simulator is chosen as our simulation environment. It is integrated with an open interface to interact with the agent in real-time. The agent can learn from numerous driving and highway situations that are created and fed to it. The representation becomes more general by randomizing and customizing the behavior of the other road users in the simulation, thus the experience of the agent can be much more diverse. The article briefly describes the modeling environment, the details on the learning agent, and the rewarding scheme. After evaluating the experiences gained from the training, some further plans and optimization ideas are briefed.

关键词： Road transportation Learning (artificial intelligence) Vehicles Training Neural networks Aerospace electronics Acceleration

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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