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Real-time power optimization based on q-learning algorithm for direct methanol fuel cell system

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 89卷 1241-1253页

作者： Chi, Xuncheng Chen, Fengxiang Zhai, Shuang Hu, Zhe Zhou, Su Wei, Wei Tongji Univ Sch Automot Studies Shanghai Peoples R China Shanghai Refire Technol Co Ltd Shanghai Peoples R China Shanghai Zhongqiao Vocat & Tech Univ Shanghai Peoples R China CAS &M Zhangjiagang New Energy Technol Co Ltd Zhangjiagang Peoples R China

Efficient real-time power optimization of direct methanol fuel cell (DMFC) system is crucial for enhancing its performance and reliability. The power of DMFC system is mainly affected by stack temperature and circulating methanol concentration. However, the methanol concentration cannot be directly measured using reliable sensors, which poses a challenge for the real-time power optimization. To address this issue, this paper investigates the operating mechanism of DMFC system and establishes a system power model. Based on the established model, reinforcement learning using q-learning algorithm is proposed to control methanol supply to optimize DMFC system power under varying operating conditions. This algorithm is simple, easy to implement, and does not rely on methanol concentration measurements. To validate the effectiveness of the proposed algorithm, simulation comparisons between the proposed method and the traditional perturbation and observation (P&O) algorithm are implemented under different operating conditions. The results show that proposed power optimization based on q-learning algorithm improves net power by 1% and eliminates the fluctuation of methanol supply caused by P&O. For practical implementation considerations and real-time requirements of the algorithm, hardware-in-the-loop (HIL) experiments are conducted. The experiment results demonstrate that the proposed methods optimize net power under different operating conditions. Additionally, in terms of model accuracy, the experimental results are well matched with the simulation. Moreover, under varying load condition, compared with P&O, proposed power optimization based on q-learning algorithm reduces root mean square error (RMSE) from 7.271% to 2.996% and mean absolute error (MAE) from 5.036% to 0.331%.

关键词： Direct methanol fuel cell (DMFC) system Real-time power optimization Methanol supply control Reinforcement learning q -learning algorithm

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Improved ACO algorithm fused with improved q-learning algorithm for Bessel curve global path planning of search and rescue robots

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ROBOTICS AND AUTONOMOUS SYSTEMS 2024年 182卷

作者： Fang, Wenkai Liao, Zhigao Bai, Yufeng Jiangsu Univ Sci & Technol Coll Econ & Management Zhenjiang 212100 Peoples R China Guangxi Univ Sci & Technol Coll Econ & Management Liuzhou 545006 Peoples R China Guangxi Univ Sci & Technol Guangxi Res Ctr High Qual Ind Dev Liuzhou 545006 Peoples R China

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved q-learning (IAC-IqL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and q-learning (qL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the q-matrix in the qL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IqL algorithm were verified. The experimental results demonstrated that the IAC-IqL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of qL algorithm. SAR robots equipped with the improved IAC-IqL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

关键词： Ant colony algorithm q -learning algorithm IAC-IqL algorithm: Bessel curve Global path planning Search and rescue robots

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qL-ADIFA: Hybrid optimization using q-learning and an adaptive logarithmic spiral-levy firefly algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 13542-13561页

作者： Tan, Shuang Zhao, Shangrui Wu, Jinran Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Australian Catholic Univ Inst Learning Sci & Teacher Educ Brisbane 4000 Australia

Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Although FA has been significantly enhanced to im-prove its performance, it still exhibits certain deficiencies. To overcome these limitations, this study presents the q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (qL-ADIFA). The q-learning technique empowers the improved firefly algorithm to leverage the firefly's environmental awareness and memory while in flight, allowing further refinement of the enhanced fire-fly. Numerical experiments demonstrate that qL-ADIFA outperforms existing methods on 15 bench-mark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020.

关键词： q -learning algorithm firefly algorithm Meta -heuristics optimization problems

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q-learning based hyper-heuristic with clustering strategy for combinatorial optimization: A case study on permutation flow-shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Yang, Yuan-yuan qian, Bin Li, Zuocheng Hu, Rong Wang, Ling Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Higher Educ Key Lab Ind Intelligence & Syst Yunnan Kunming 650500 Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

In this paper, a q-learning based hyper-heuristic with clustering strategy (qHH/CS) is proposed for combinatorial optimization problems (COPs). In qHH/CS, a clustering strategy based on low-dimensional mapping method is devised to map initial population to a low-dimensional space, thus obtaining multiple subpopulations accounting for different search directions. To discover more promising search regions around each subpopulation, we propose a parallel q-learning search mechanism composed of multiple search components, including multi-subpopulation q-table, state extraction method, contribution-driven reward function, and deep mining local search actions. Relying on these search components, qHH/CS identifies the variations of the objective values of subpopulations to evaluate the solution features of COPs, whereby valuable information can be learned during the search process of the algorithm. To illustrate the effectiveness of qHH/CS, it is applied to solve the permutation flow-shop scheduling problem. We additionally assess qHH/CS through the well-known vehicle routing problem, which confirms the general search ability of the algorithm for COPs. Moreover, the convergence analysis of the qHH/CS algorithm is performed, providing theoretical guidance for the optimization process of the proposed algorithm. Results of experiments demonstrate that qHH/CS can find high-quality solutions to the solved problems.

关键词： q -learning algorithm Hyper-heuristic Low-dimensional mapping Clustering strategy Combinatorial optimization

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Study on a new metaheuristic algorithm - Tribal intelligent evolution optimization and its application in optimal control of cooling plants

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APPLIED ENERGY 2025年 383卷

作者： Yao, Ye Hong, Xiaoxi Xiong, Lei Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Sch Mech Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

This study proposes a new metaheuristic algorithm called Tribal Intelligent Evolution Optimization (TIEO). This algorithm is inspired by the process of continuous division and unification of tribes in history. The fundamental theory and structure of the TIEO are illustrated in detail. The superiority of the algorithm has been verified against a set of standard test functions through experiments and comparisons with the other optimization algorithms such as PSO, DE, WPS, SSA, SBO, SCA, GSA, etc. Through the comprehensive evaluation of optimization accuracy, standard deviation and calculation time, the proposed TIEO algorithm exhibits the best performance among the typical optimization algorithms. Furthermore, a sensitivity analysis of key hyperparameters in the new algorithm has been conducted, and the recommended ranges for each hyperparameter are provided. The iterative formula of the algorithm has been discussed from a mathematical perspective, and the probability of improving parameter fitness has been tested. Finally, the proposed TIEO has been successfully applied to the optimal control of a central HVAC cooling plant, achieving energy-saving rates of 19.43 %, 12.94 %, and 17.9 % under low, medium, and high load conditions, respectively, which are higher than those based on the other standard optimization algorithms. This work contributes to the development of the metaheuristic optimization algorithm.

关键词： Tribal intelligent evolution optimization q -learning algorithm K -means algorithm Optimal control

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Non-contact vision-based response reconstruction and reinforcement learning guided evolutionary algorithm for substructural condition assessment

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Zhang, Guangcai Hou, Jiale Wan, Chunfeng Li, Jun Xie, Liyu Xue, Songtao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Curtin Univ Ctr Infrastruct Monitoring & Protect Sch Civil & Mech Engn Bentley Australia Tsinghua Univ Dept Civil Engn Beijing Peoples R China Southeast Univ Adv Ocean Inst Nantong Peoples R China Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China Tohoku Inst Technol Dept Architecture Sendai Japan

Structural health monitoring of large-span bridges and high-rise buildings is crucial for ensuring safety and serviceability. However, accurately capturing motion in these structures using consumer-grade cameras is challenging due to their limited Field of Vision (FOV). To address this issue, in this study, a novel output-only substructural condition assessment framework based on the reinforcement learning-guided evolutionary algorithm and vision-based displacement response reconstruction technique is proposed. On the one hand, displacement responses of the target substructure are extracted from the vibration video using subpixel template matching algorithm with camera pose correction, which is suitable for integration with substructure strategy to detect elemental damage. A vision-based substructural displacement response reconstruction technique is developed based on transmissibility matrix and Tikhonov regularization. The measured and reconstructed displacements are utilized to established the objective function. On the other hand, to solve the optimization-based damage identification problem, a new reinforcement learning guided evolutionary algorithm, named sparse q-learning guided evolutionary algorithm (SqEA), is proposed. In the proposed SqEA, sparse initial population is produced by reducing the dimension of unknown parameters to be identified. Six different search strategies, including DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, Jaya mutation, perturbation with the Cauchy mutation, are used to construct a search strategy pool. A representative reinforced learning algorithm, q-learning algorithm is introduced to adaptively select the most suitable search strategy. Experimental tests on a steel frame structure and a three-span beam structure are performed to validate the accuracy, efficiency, and robustness of the proposed approach. Results demonstrate that the damage locations and extents can be accurately identified without the measurement of input forces,

关键词： Substructural condition assessment Vision-based measurement Response reconstruction Template matching algorithm Sparse population initialization q -learning algorithm

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Reinforcement learning based adaptive control method for traffic lights in intelligent transportation

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ALEXANDRIA ENGINEERING JOURNAL 2024年 106卷 381-391页

作者： Huang, Zhongyi London Sch Econ & Polit Sci Geog & Environm Dept London WC2A 2AE England

Addressing the requirements and challenges of traffic light control, a reinforcement learning based adaptive optimal control model for traffic lights in intelligent transportation systems is proposed. In the model design, we combined Markov decision process, q-learning algorithm, and Deep q-learning Network (DqN) control theory to establish a comprehensive signal light Adaptive Optimal Control of Signal Lights in Intelligent Transportation Systems (AOCITL) control model. Through simulation experiments on the model and the application of actual road scene data, we have verified the superiority of the model in improving traffic system efficiency and reducing traffic pressure. The experimental results show that compared with traditional fixed cycle signal light control, the adaptive optimal control model based on reinforcement learning can significantly improve the traffic efficiency of roads, reduce the incidence of traffic accidents, and enhance the overall operational effectiveness of urban transportation systems. The proposed method is possible to further optimize the model algorithm, expand its application scope, and promote the development and practical application of intelligent transportation systems.

关键词： Machine learning Transportation engineering Intelligent transportation system q -learning algorithm DqN control theory

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Application of self-improving q-learning controller for a class of dynamical processes: Implementation aspects

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Musial, Jakub Stebel, Krzysztof Czeczot, Jacek Nowak, Pawel Gabrys, Bogdan Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Automat Control & Robot PL-44100 Gliwice Poland Univ Technol Sydney Data Sci Inst Complex Adapt Syst Lab Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This paper deals with a practical application of the q-learning algorithm as a general-purpose self-improving controller operating in a class of industrial closed-loop control systems. The proposed approach solves a practical control engineering problem by designing a controller that starts its operation with the predefined closed-loop control performance and then, without unnecessarily disturbing the normally operated closed-loop system, it learns on-line from interactions with the controlled process to ensure the gradual improvement in the closed loop control performance until this performance reaches a desired user-defined level. It is proposed how to ensure its initial performance by an appropriate initialization of the q-matrix without assuming any knowledge about the process model. The desired target closed-loop performance is defined by the first-order reference trajectory that should be preserved by the closed-loop system, which requires determining the current state of the closed-loop system by the values of the control error and its time derivative. The novelty of the proposed approach results from: (i) preserving the first order reference trajectory with reduced dimensions of the q-matrix to two, which significantly reduces memory and computational requirements without any loss of generality, (ii) proposing a method for a reduction of the number of states designed directly for the proposed approach, and (iii) proposing a method for a very convenient initialization of the q-matrix based only on the tunings of the existing PI controller. All these features bring the proposed approach closer towards practical applications. Results of the simulation and experimental validation show that the proposed q-learning controller can substitute the existing PI controller bumplessly and provides a gradual improvement in the closed loop performance due to its online learning abilities.

关键词： q -learning algorithm Reinforcement learning Process control Online learning Intelligent control Practical validation

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Fault location of transmission line based on CNN-LSTM double-ended combined model

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ENERGY REPORTS 2022年 8卷 781-791页

作者： Wang, Xinghua Zhou, Peng Peng, Xiangang Wu, Zelin Yuan, Haoliang Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In the study of transmission line fault location, most of the previous artificial intelligence-based location methods rely heavily on feature extraction of fault signals, which depend on the researcher's level of analytical understanding of fault characteristics and require some experience. In addition, previous location methods are more sensitive to line parameters, and the machine learning model obtained based on a specific line is not applicable to other lines, which restricts the application of the method. To solve the above problems, this paper proposes a double-ended combined fault location model based on Maximum Mean Discrepancy (MMD), which combined Convolutional Neural Network(CNN) and Long Short-Term Memory(LSTM). First, different transmission lines are categorized by MMD. Second, a double-ended CNN-LSTM combination model is built for similar lines, which autonomously extracts fault features in an end-to-end form, and then the weights of combination model are determined by the q-learning algorithm. Finally, we obtain the fault distance prediction. Simulation studies show that the CNN-LSTM double-ended combined model based on MMD has good generalization performance for lines with different parameters, cracking the problem of specialized modeling of different lines while meeting the requirement of fault location accuracy. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd International Conference on Power Engineering, ICPE, 2021.

关键词： Transmission line Fault location Maximum mean distance Convolutional Neural Network Long Short-Term Memory q -learning algorithm

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Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks

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KNOWLEDGE-BASED SYSTEMS 2023年第1期264卷

作者： Gholami, Hadi Sun, Hongyang Univ Kansas Lawrence KS 66045 USA

Due to the large volume of requests and the need to speed up the provision of services, production companies are migrating from a single service center to distributed centers. To support this migration, it is necessary to make intelligence decisions that benefit from automatic design of search algorithms. Considering these, this paper addresses the distributed hybrid flow shop scheduling problem with multiprocessor tasks (DHFSP-MT) as an extension of the hybrid flow shop scheduling problem with multiprocessor tasks (HFSP-MT) to minimize the maximum completion time among distributed factories. To provide effective decision support, we apply a novel framework called conditional markov chain search (CMCS) to automate the generation of heuristics, which is presented for the first time in the distributed shop scheduling problem to the best of our knowledge. We express the HFSP-MT as a markov decision process (MDP) and solve it through a hybrid q-learning-local search algorithm. By using the characteristics of the problem under study, we introduce two new concepts, weight and impact, which are used to develop an initial construction algorithm and two local search methods. To balance jobs between factories at runtime, we propose a load balancing method, which transfers selected jobs from certain source factories to destination factories. We compare the proposed CMCS with two state-of-the-art metaheuristic algorithms from the literature using publicly available benchmark instances. The computational results show that the proposed CMCS provides better performance than that of the existing algorithms on solving the considered DHFSP-MT.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Automated algorithm design Conditional markov chain search Distributed hybrid flow shop scheduling Multiprocessor tasks q -learning algorithm

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