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Integrated information system based on q-learning algorithm and multi-objective particle swarm optimization with molecular fuzzy-based decision-making for corporate environmental investments

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INFORMATION SCIENCES 2025年 698卷

作者： Dincer, Hasan Yuksel, Serhat Olaru, Gabriela Oana Eti, Serkan Istanbul Medipol Univ Sch Business Istanbul Turkiye Istanbul Medipol Univ Sch Commun Istanbul Turkiye Istanbul Medipol Univ IMU Vocat Sch Istanbul Turkiye Khazar Univ Res Ctr Sustainable Econ Dev Baku Azerbaijan

Determining the most important criteria is a great necessity to increase the environmental performance of renewable energy projects. This situation helps to reach cost efficiency and effective use of limited resources. However, there are very few studies in the literature where priority analysis is made for these factors. This condition indicates an important gap in the literature on this subject. Accordingly, this study aims to identify the most convenient investment strategies to increase the environmental performance of renewable energy projects. First, the balanced expert dataset is generated via q-learning algorithm and multi-objective particle swarm optimization. In the following step, selected criteria are weighted with molecular fuzzy cognitive maps approach. The final stage consists of ranking the alternatives with fuzzy molecular ranking (MORAN). The contribution of this study to the literature is to define the most appropriate investment strategies to increase the environmental performance of these projects by generating a new decision-making model. Integrating molecular geometry into decision-making processes increases both the originality and effectiveness of the model. Thus, it is possible to calculate the degrees with a more scientific infrastructure. Integrating q-learning and swarm optimization techniques into the decision-making model also increases the superiority of the model. In this context, the optimization technique is used to obtain the importance weights of the experts. The findings denote that energy capacity expansion plays the most important role in improving the environmental performance of businesses because it has the greatest weight (0.2563). Return on investment is another factor that plays an important role in increasing this performance with the weight of 0.2559. Geothermal and hydropower are found as the most successful renewable energy types regarding environmental performance improvement since they have the highest aggregated v

关键词： q-learning algorithm Molecular fuzzy sets MORAN Swarm optimization Environmental performance

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Personalized Sports Health Recommendation System Assisted by q-learning algorithm

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第4期41卷 1889-1901页

作者： Yang, Yang Zhao, Yuanji Chengdu Sport Univ Coll Phys Educ Chengdu Peoples R China Wuhan Sports Univ Sch Phys Educ Wuhan Peoples R China

In response to the current problem of single sports plan and lack of long-term motivation in recommendation systems, a more intelligent personalized sports health recommendation system was designed by introducing q-learning (quality learning) algorithm. Firstly, user sports health data was collected, and the user model was constructed to track user sport preferences and historical behavior. Secondly, the sports environment was defined, including different types of sports activities, venues, and weather. Then, the reward function was formulated to reward and punish users based on their sports activities and goals, in order to maximize long-term health benefits. Finally, the q-learning algorithm was implemented to continuously iteratively learn and optimize user recommendation models to provide the best personalized sports recommendations. For personalized accuracy, indicators such as precision, recall, F1 value, MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) were used to evaluate, while the system's participation in sports, user satisfaction, long-term incentive effects, and overall health improvement were collected. The results showed that the average precision of the recommendation system on 10 different datasets was 88%, and the average AUC (Area Under Curve) was 96%, which was 6.7% higher than the SVD (Singular Value Decomposition) algorithm. The user's sports persistence rate was improved by 25%, and the health score was improved by about 13.3%. These data not only reflect the superior performance of the recommendation system but also highlight its positive impact on long-term user motivation and overall health levels. The results indicate that the proposed personalized exercise health recommendation system, assisted by the q-learning algorithm, has significantly improved accuracy. Moreover, it offers users more intelligent and personalized exercise suggestions, effectively increasing long-term participation in physical activities and overall healt

关键词： Sports health system q-learning algorithm personalized recommendations user modeling long-term motivation

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Compatibility and Performance Improvement of the WPT Systems Based on q-learning algorithm

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第8期39卷 10582-10593页

作者： Liu, Xu Chao, Jie Rong, Cancan Liao, Zhijuan Xia, Chenyang China Univ Min & Technol Sch Elect Engn Xuzhou 221116 Peoples R China

Different kinds of compensation topologies are widely used in wireless power transfer (WPT) systems, resulting in loads with different compensation topologies hardly obtaining the same power from the same power transmitter. In addition, the load power and system efficiency are hard to hold constant when the mutual inductance or load resistance varies, and these two variation parameters are both hard to be accurately identified. These severely limit the practical popularization of the WPT technology. Therefore, a new strategy to improve the compatibility and performance of the WPT system based on the q-learning algorithm and switch-controlled-capacitor (SCC) is proposed in this article. The q-learning algorithm is used to train an intelligent offline database by monitoring the voltage and current in the primary side of the system to obtain an optimized compensation capacitor on the primary side without knowing the mutual inductance and load resistance. A 1-kW prototype is established and the experimental results demonstrate that the load side can receive desired power when the load changes from one kind of compensation topology to another, or the load resistance and mutual inductance vary together. Meanwhile, the coil-to-coil efficiency can also be effectively maintained.

关键词： Topology Transmitters Resistance q-learning Inductance Mathematical models Coils Closed-loop control compatibility performance optimization q-learning algorithm wireless power transfer (WPT)

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Multi-AGV route planning in automated warehouse system based on shortest-time q-learning algorithm

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ASIAN JOURNAL OF CONTROL 2024年第2期26卷 683-702页

作者： Zhang, Zheng Chen, Juan Zhao, Wenbing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Goldwind Sci & Technol Co Ltd R&D Ctr Beijing Peoples R China Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA

Route planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard-like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy-saving routes. Although the traditional route planning algorithms such as the classical q-learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi-AGV route planning approach to solving the AGV route planning problem in the chessboard-like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low-dimensional adjacency-reward matrix for route planning. This algorithm improves the efficiency of classical q-learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi-AGV online collision-free route planning algorithm based on these considerations for dynamic route planning for multi-AGVs operating in a large-scale warehouse. The proposed algorithms are validated with several case studies.

关键词： AGV automated warehouse collision-free q-learning algorithm route planning shortest time

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q-learning algorithm Enabled Topology Control Scheme in Power Line Communication Networks 4

Q-Learning Algorithm Enabled Topology Control Scheme in Powe...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Liu, Lin Zheng, Libin Wang, Yusi State Grid Dalian Elect Power Supply Co Dalian Liaoning Peoples R China Beijing SmartChip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665489577

The topology control technology is of great importance in the Power Line Communications (PLC) networks for smart power grid. Due to the weak topology controlling ability of current large-scale PLC networks, the throughput of current PLC networks is limited seriously and fail to support more real-time services. To deal with this problem, this paper proposes a q-learning algorithm enabled topology control scheme of PLC networks. This robust topology control scheme introduces the q-learning algorithm into the CSMA/CA protocol and adopts the Markov process to build the networking model of PLC system. Through period on-line learning by proposed algorithm, the robust topology can be established in PLC networks. Test results show that the proposed approach can improve topology control ability for real-time services by smart grid IoT (SG-IoT) systems, in terms of both packet loss rate and time delay performance with great feasibility and efficiency.

关键词： Topology control scheme q-learning algorithm power line communications smart grid IoT

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Ant Colony Enhanced q-learning algorithm For Mobile Robot Path Planning 36

Ant Colony Enhanced Q-Learning Algorithm For Mobile Robot Pa...

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36th Chinese Control and Decision Conference (CCDC)

作者： Xie, Tian Zhou, Yi Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

q-learning algorithm is a common method for solving robot path planning problems. However, it is not only significantly time-consuming, but also prone to getting stuck in local optima. This paper presents an enhanced approach to the q-learning algorithm by incorporating ant colony optimization, which leads to better convergence of the q-learning algorithm and reduces the number of iterations and computational time. Firstly, the q-table and initial paths are constructed through interactive training between the agents and the environment. Secondly, the ant colony algorithm dynamically updates the pheromone matrix and iteratively adapts the path search strategy in the q-table. Experimental results illustrate that the enhanced q-learning algorithm exhibits superior performance compared to the original q-learning algorithm in path planning simulations, as indicated by three evaluation metrics. Furthermore, improvements are observed in terms of the length of the shortest path and the quantity of turning points, yielding smoother paths.

关键词： Mobile Robot Reinforcement learning Path Planning q-learning algorithm Ant Colony algorithm

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Optimization of Control for Stepwise Lifting Grounding Plier Using q-learning algorithm 24

Optimization of Control for Stepwise Lifting Grounding Plier...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Chen, Xingsong Bai, Juan Guo, Zipei Chi, Xiaobing Wang, Deyin Zou, Yu China Southern Power Grid Co Ltd Guangxi Qinzhou Power Supply Bur Qinzhou 535000 Guangxi Peoples R China

ISBN: (纸本)9798400718144

This article focuses on the problem of traditional grounding plier control lacking intelligent optimization algorithm support and difficult to adapt to complex environments. Based on the q-learning (quality learning) algorithm, the optimization of stepwise lifting grounding plier control is studied. This article first defines the state space and action space of the lifting grounding plier, and applies discretization to adapt to the q-learning algorithm. Subsequently, a reward function is designed, with safety, efficiency, and energy consumption as indicators to guide the algorithm in learning the optimal control strategy. By establishing a q-table and continuously updating it, the optimization of grounding plier control has been achieved. In the experiment, the optimized lifting speed is increased to 0.4m/s, 0.5m/s, and 0.4m/s. The lowering time is shortened to 10s, 10s, and 12.5s, and the number of shaking is significantly reduced. In the response time experiment to reduce the load, under the condition of reducing the load by 2kg, the optimized reduced response times are 0.98s, 0.96s, and 0.97s, reflecting the good adaptability of the lifting grounding plier after algorithm optimization. The experimental results show that the q-learning algorithm has a good effect on improving the control performance of grounding pliers, providing a reference for similar research.

关键词： q-learning algorithm Control Optimization Reward Function State Space and Action Space Optimal Control Strategy

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Optimal Economic Dispatch Scheduling in Competitive Energy Market Utilizing a Greedy q-learning algorithm

Optimal Economic Dispatch Scheduling in Competitive Energy M...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Arvanitidis, Athanasios Ioannis Alamaniotis, Miltiadis University of Texas at San Antonio Electrical & Computer Engineering San Antonio United States

ISBN: (纸本)9789531842976

In the realm of deregulated electricity markets, selection of power plant units for electricity generation is considered crucial for their optimal operation. In this study, an agent-involved model simulating a competitive energy market consisted of two generation companies aiming to cover load demand and maximize revenue is examined. The agent is trained through the utilization of the q-learning algorithm embedded with a greedy reward system. The results clearly illustrate that the agent driven supplier attains the optimal economic dispatch schedule, underscoring the effectiveness of a single training session, which enables the agent to learn the most cost-effective approach to cover maximum capacity while simultaneously minimizing testing errors. Finally, the findings of this study emphasize the potential for improved decision-making for more autonomous economic dispatch processes in the dynamic landscape of competitive energy markets. © 2024 IEEE.

关键词： agent-involved model deregulated electricity markets economic dispatch schedule q-learning algorithm

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Hyper-heuristic q-learning algorithm for Flow-Shop Scheduling Problem with Fuzzy Processing Times 19th

Hyper-heuristic Q-Learning Algorithm for Flow-Shop Schedulin...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zhu, Jin-Han Hu, Rong Li, Zuo-Cheng qian, Bin Zhang, Zi-qi Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

The flow-shop scheduling problem (FSP) has been extensively studied over the past few decades. q-learning algorithms (qLAs) have been shown to be effective in evolving efficient heuristics for scheduling problems. In this paper, we propose a hyper-heuristic q-learning algorithm (HHqLA) for the FSP with fuzzy processing times (FSPF) to minimize the fuzzy makespan. FSPF accounts for the existence of various disturbing factors in the manufacturing industry thatmake the processing time fuzzy. To capture this, we use triangular fuzzy numbers (TFNs) to express the processing time more realistically. Firstly, four low-level heuristics (LLHs) are designed based on the characteristics of FSPF, and the permutations of the LLHs are used as high-level individuals. Secondly, the extraction of valuable information from excellent high-level individuals by using the q-learning algorithm (qLA). Thirdly, a newsampling strategy is designed to apply the information obtained by qLA to generate new high-level individuals and search for potential regions in the solution space. Finally, the experimental results demonstrate that the proposed HHqLA can effectively solve FSPF.

关键词： Fuzzy processing times Hyper-heuristic algorithm q-learning algorithm

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An optimized q-learning algorithm for mobile robot local path planning

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KNOWLEDGE-BASED SYSTEMS 2024年 286卷

作者： Zhou, qian Lian, Yang Wu, Jiayang Zhu, Mengyue Wang, Haiyong Cao, Jinli Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Sch Software Nanjing 210037 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Cyberspace Secur Nanjing 210037 Jiangsu Peoples R China La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Australia

The q-learning algorithm is a reinforcement learning technique widely used in various fields such as path planning, intelligent transportation, penetration testing, among others. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Most non-agent-based path planning algorithms face challenges in exploring completely unknown environments effectively, lacking efficient perception in unfamiliar settings. Additionally, many qlearning-based path planning algorithms suffer from slow convergence and susceptibility to getting stuck in local optimal solutions. To address these issues, an optimized version of the q-learning algorithm (Optimized q-learning, O-qL) is proposed and applied to local path planning of mobile robots. O-qL introduces novel q -table initialization methods, incorporates a new action-selection policy, and a new reward function, and adapts the Root Mean Square Propagation (RMSprop) method in the learning rate adjustment. This adjustment dynamically tunes the learning rate based on gradient changes to accelerate learning and enhance path planning efficiency. Simulation experiments are carried out in three maze environments with different complexity levels, and the performance of the algorithm in local path planning is evaluated using steps, exploration reward, learning rate change and running time. The experimental results demonstrate that O-qL exhibits improvements across all four metrics compared to existing algorithms.

关键词： Mobile robot q-learning algorithm Local path planning Reinforcement learning Adaptive learning rate

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