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A reinforcement learning-based dynamic multi-objective optimization approach for integrated timetabling and vehicle scheduling

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Knowledge-Based Systems 2025年 321卷

作者： Shen, Yindong Xie, Wenliang Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China The MOE Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Dynamic integrated timetabling and vehicle scheduling (D-ITVS) is essential for mitigating the negative impacts of service disruptions. It involves multiple rescheduling stages, with inherent optimization similarities across these stages. However, existing optimization approaches for the D-ITVS problem have not systematically exploited these similarities, overlooking the potential for decision knowledge from previous stages to inform the current stage. To address this gap, this paper proposes a reinforcement learning-based dynamic multi-objective optimization approach (RL-DMOA), which focuses on transferring decision knowledge between rescheduling stages. This approach models the optimization process of each rescheduling stage in the D-ITVS problem as a Markov decision process, incorporating a state space with vehicle information, action space for vehicle assignment, and a multi-objective reward function. A multi-objective deep reinforcement learning (M-DRL) agent is employed within the RL-DMOA to select actions based on the state at each decision point. The agent is constructed on a multi-objective deep Q-learning network (M-DQN), with a Q-value adjustment layer incorporated to prevent the selection of invalid actions. To select optimal actions while balancing the conflicts among multiple objectives, the M-DRL agent applies a non-dominated sorting selection strategy. Experimental results demonstrate that the proposed RL-DMOA is capable of generating timetables and vehicle schedules with reduced costs, enhanced robustness, and improved convergence and diversity across all rescheduling stages. By balancing operational costs and passenger service quality, these improvements benefit transit operators, and during daily operations, passengers enjoy reduced travel costs and enhanced service reliability. © 2025 Elsevier B.V.

关键词： Dynamic multi-objective optimization Reinforcement learning Timetabling Vehicle scheduling

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Exponential synchronization of coupled memristive neural networks via pinning control

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Chinese Physics B 2013年第5期22卷 203-212页

作者：王冠沈轶尹泉 Department of Control Science and Engineering Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control

This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

关键词： synchronization memristor coupled neural networks pinning control

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： image quality Quantization (signal) image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Adaptive Backstepping Slide Mode control of Pneumatic Position Servo System

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Chinese Journal of Mechanical Engineering 2016年第5期29卷 1003-1009页

作者： REN Haipeng FAN Juntao Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi'an University of Technology

With the price decreasing of the pneumatic proportional valve and the high performance micro controller, the simple structure and high tracking performance pneumatic servo system demonstrates more application potential in many fields. However, most existing control methods with high tracking performance need to know the model information and to use pressure sensor. This limits the application of the pneumatic servo system. An adaptive backstepping slide mode control method is proposed for pneumatic position servo system. The proposed method designs adaptive slide mode controller using backstepping design technique. The controller parameter adaptive law is derived from Lyapunov analysis to guarantee the stability of the system. A theorem is testified to show that the state of closed-loop system is uniformly bounded, and the closed-loop system is stable. The advantages of the proposed method include that system dynamic model parameters are not required for the controller design, uncertain parameters bounds are not need, and the bulk and expensive pressure sensor is not needed as well. Experimental performance, as compared with some existing methods. results show that the designed controller can achieve better tracking

关键词： pneumatic position servo system adaptive backstepping design slide mode control uncertain parameter tracking accuracy

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Immune particle swarm optimization of linear frequency modulation in acoustic communication

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Journal of Systems Engineering and Electronics 2015年第3期26卷 450-456页

作者： Haipeng Ren Yang Zhao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate （BER）. To solve this problem, carrier waveform inter- displacement （CWlD） modulation is proposed. It has been proved that CWlD modulation is an effective method to decrease BER. The linear frequency modulation （LFM） carrier-waves are used in CWlD modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization （IPSO） is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multi- peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved perfor- mance and effectiveness of the optimization method.

关键词： underwater acoustic communication carrier waveform inter-displacement （CWlD） multi-objective optimization immune particle swarm optimization （IPSO）.

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Reversible spiking neural P systems

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Frontiers of Computer Science 2013年第3期7卷 350-358页

作者： Tao SONG Xiaolong SHI Jinbang XU Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Spiking neural （SN） P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.

关键词： membrane computing spiking neural P system reversible computing model universality reversible register machine

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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Changbing Pan, Linchao Chen, Jie Liu, Yang Lai, Jingang The College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China The School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China The Hangzhou School of Automation Zhejiang Normal University Hangzhou311231 China The College of Electronic Engineering National University of Defense Technology Hefei230037 China The School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Education Ministry of China China Huazhong University of Science and Technology Wuhan430074 China

To execute a variety of collaborative tasks, the cooperation for unmanned aerial vehicles (UAVs) with complicated interactions under dynamic environments is a challenging and critical issue. This paper studies the cooperation issue for UAVs under dynamic environments through an approach of game theory-reinforcement learning (GT-RL) approach, which combines the advantages of both game theory and reinforcement learning. First, to cope with the complicated interactions of UAVs, the cluster of UAVs is modeled as a public goods game with asymmetrical environmental feedback. Then, reinforcement learning is adopted to optimize decision-making of UAVs under unknown and dynamic environments through comparing the dynamic behaviors of UAVs, where a novel dynamics system offers a more comprehensive understanding on cooperative behavior among UAVs. Finally, the simulation results show that the GT-RL approach can effectively promote cooperation among UAVs in completing the collaborative tasks. © 1967-2012 IEEE.

关键词： Adversarial machine learning

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Quantum Immune Clonal Selection Algorithm for Multi-objective 0/1 Knapsack Problems

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Chinese Physics Letters 2010年第1期27卷 37-40页

作者：尚荣华焦李成李阳阳吴建设 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071

Based on the concept and principles of quantum computing and the principle of the immune clonal selection, a new algorithm for multi-objective 0/1 knapsack problems is introduced. In the algorithm, for the novel representation, qubit antibodies in the antibody population are updated by applying a new chaos update strategy. A quantitative metric is used for testing the convergence to the Pareto-optimal front. Simulation results on the 0/1 knapsack problems show that the new algorithm, in most cases, is more effective.

关键词： Chinese climate network complex systems small world community

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An immune memory clonal algorithm for numerical and combinatorial optimization

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中国计算机科学前沿 2010年第4期4卷 536-559页

作者： Ruochen LIU Licheng JIAO Yangyang LI Jing LIU Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information ProcessingXidian UniversityXi'an 710071China

Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonai algorithm (IMCA). The clonal operator,inspired by the immune system, is discussed first. The IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm (AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time. By using the clonal selection operator, global searching is effectively combined with local *** to the antibody-antibody (Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs)are used to validate the performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1.

关键词： artificial immune system (AIS) clonal selection immune memory immune network model evolutionary computation knapsack problem(KP) traveling salesman problem(TSP)

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Dual-mode predictive control of a rotor suspension system

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Science China(information Sciences) 2020年第1期63卷 178-189页

作者： Yue WU Gui-Ping REN Hai-Tao ZHANG Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipments and Technology

Rotor active magnetic bearing(rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotorAMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller(DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closedloop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

关键词： dual-mode predictive control system identification optimization control model predictive control suspension control

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