Search Results (386)

The rapid development of the new energy vehicle industry has resulted in a significant number of waste electric vehicle batteries (WEVBs) reaching the end of their useful life. The recycling of these batteries holds both economic and environmental value. As policy is a critical factor influencing the recycling of waste electric vehicle batteries, its role in the network warrants deeper investigation. Based on this, this study integrates both subsidy and penalty policy into the design of the waste electric vehicle battery reverse logistics network (RLN), aiming to examine the effects of single policy and policy combinations, thereby filling the research gap in the existing literature that predominantly focuses on single-policy perspectives. Considering multiple battery types, different recycling technologies, and uncertain recycling quantities and qualities, this study develops a fuzzy mixed-integer programming model to optimize cost and carbon emission. The fuzzy model is transformed into a deterministic equivalent form using expected intervals, expected values, and fuzzy chance-constrained programming. By normalizing and weighting the upper and lower bounds of the multi-objective functions, the model is transformed into a single-objective optimization problem. The effectiveness of the proposed model and solution method was validated through an empirical study on the construction of a waste electric vehicle battery reverse logistics network in Zhengzhou City. The experimental results demonstrate that combined policy outperforms single policy in balancing economic benefits and environmental protection. The results provide decision-making support for policymakers and industry stakeholders in optimizing reverse logistics networks for waste electric vehicle batteries. Full article

The optimal water level prediction and control of the Great Lakes is critical for balancing ecological, economic, and societal demands. This study proposes a multi-objective planning model integrated with a fuzzy control algorithm to address the conflicting interests of stakeholders and dynamic hydrological complexities. First, a network flow model is established to capture the interconnected flow dynamics among the five Great Lakes, incorporating lake volume equations derived from paraboloid-shaped bed assumptions. Multi-objective optimization aims to maximize hydropower flow while minimizing water level fluctuations, solved via a hybrid Ford–Fulkerson and simulated annealing approach. A fuzzy controller is designed to regulate dam gate openings based on water level deviations and seasonal variations, ensuring stability within ±0.6096 m of target levels. Simulations demonstrate rapid convergence (T = 5 time units) and robustness under environmental disturbances, with sensitivity analysis confirming effectiveness in stable conditions (parameter ≥ 0.2). The results highlight the framework’s capability to harmonize stakeholder needs and ecological sustainability, offering a scalable solution for large-scale hydrological systems. Full article

Synthetic Aperture Radar (SAR) constellations have become a key technology for disaster monitoring, terrain mapping, and ocean surveillance due to their all-weather and high-resolution imaging capabilities. However, the design of large-scale SAR constellations faces multi-objective optimization challenges, including short revisit cycles, wide coverage, high-performance imaging, and cost-effectiveness. Traditional optimization methods, such as genetic algorithms, suffer from issues like parameter dependency, slow convergence, and the complexity of multi-objective trade-offs. To address these challenges, this paper proposes a hybrid optimization framework that integrates chaotic sequence initialization and fuzzy rule-based decision mechanisms to solve high-dimensional constellation design problems. The framework generates the initial population using chaotic mapping, adaptively adjusts crossover strategies through fuzzy logic, and achieves multi-objective optimization via a weighted objective function. The simulation results demonstrate that the proposed method outperforms traditional algorithms in optimization performance, convergence speed, and robustness. Specifically, the average fitness value of the proposed method across 20 independent runs improved by 40.47% and 35.48% compared to roulette wheel selection and tournament selection, respectively. Furthermore, parameter sensitivity analysis and robustness experiments confirm the stability and superiority of the proposed method under varying parameter configurations. This study provides an efficient and reliable solution for the orbital design of large-scale SAR constellations, offering significant engineering application value. Full article

Motor vehicles emit a large amount of air pollutants. Inspection and Maintenance (I/M) systems serve as a pivotal strategy for mitigating emissions from operational diesel trucks. However, the prevalent issue of blind repairs persists due to insufficient diagnostic capabilities at maintenance stations (M stations). To address this challenge, a multi-source information fusion methodology is proposed, integrating load deceleration testing from inspection stations (I stations), on-board diagnostics (OBD) data, and manual measurements at M stations. Critical diagnostic parameters—including nitrogen oxides (NOx) and particulate matter (PM) emissions, the ratio of measured wheel-side power to rated power, intake volume, common rail pressure, and exhaust back pressure—are systematically selected through statistical analysis and expert evaluations. An adaptive membership function is developed to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework. Validation using 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data) demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%. By minimizing unnecessary repairs and optimizing maintenance efficiency, this approach significantly reduces resource waste and the lifecycle environmental footprints of diesel fleets. The proposed fuzzy-logic-based model effectively detects latent faults during routine maintenance, directly contributing to sustainable transportation through reductions in NOx and PM emissions—critical for improving air quality and advancing global climate objectives. This establishes a scalable technical framework for the effective implementation of I/M systems in alignment with sustainable urban mobility policies. Full article

It is crucial to simulate land use change and assess the corresponding impact on ecosystem services to develop informed land management policies and conservation strategies. To comprehensively simulate the patterns of land use change under different management policies and evaluate the corresponding ecological service values (ESV), a method for coupling the Generalized Multi-Objective Programming (GMOP) model and Patch-generating Land Use Simulation (PLUS) model is proposed in this study. First, the GMOP model is used to obtain optimized land use solutions under different scenarios. Then, the PLUS model is used to analyze the mechanism driving land expansion, explore land conversion patterns, and, ultimately, achieve spatial expression of land use quantity changes. The uncertain parameters in the coupled model are processed by intuitionistic fuzzy numbers. The coupled model successfully integrates the outstanding spatiotemporal dynamic simulation capability of the PLUS model and the multiobjective optimization advantages of the GMOP model, effectively overcoming the limitations of applying a single model in land use analysis. Finally, four different scenarios are established for land use change, namely, business as usual (BAU), economic efficiency priority (RED), ecological protection priority (ELP), and coordinated economic and ecological development (EEB), to predict land use change trends and ecological service values. A case study of the Ningxia Hui Autonomous Region demonstrates that the area of agricultural land exhibits a stable growth trend in the four different scenarios, with the majority of the expansion occurring through the conversion of grassland. Concurrently, the rate of expansion of construction land is highest in the BAU scenario at 31.72%, compared with the area in 2020. This is notably higher than the rates observed in the RED (10.10%) and EEB (9.47%) cases. With the expansion of construction land, the ESV decreased by 3.485 billion, 1.514 billion, and 1.658 billion yuan in the BAU, RED, and ELP scenarios, representing 41.72%, 24.96%, and 34.05% decreases in ESV, respectively. The proposed integrated methodology accounts for various spatial constraints and land conversion behaviors, thereby ensuring a true and accurate reflection of land use dynamics. This methodology supports the quantification of ESV under different land management strategies, thereby providing policymakers with effective support for data-driven sustainable land use planning and conservation. Full article

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios. Full article

Software defect prediction identifies defect-prone modules before testing, reducing costs and development time. Machine learning techniques are widely used, but high-dimensional datasets often degrade classification accuracy due to irrelevant features. To address this, effective feature selection is essential but remains an NP-hard challenge best tackled with heuristic algorithms. This study introduces a binary, multi-objective starfish optimizer for optimal feature selection, balancing feature reduction and classification performance. A Choquet fuzzy integral-based ensemble classifier further enhances prediction reliability by aggregating multiple classifiers. The approach was validated on five NASA datasets, demonstrating superior performance over traditional classifiers. Key software metrics—such as design complexity, operators and operands count, lines of code, and numbers of branches—were found to significantly influence defect prediction. The results show that the proposed method improves classification performance by 1% to 13% while retaining only 33% to 57% of the original feature set, offering a reliable and interpretable solution for software defect prediction. This approach holds strong potential for broader, high-dimensional classification tasks. Full article

This study presents a novel approach to managing the cost–time–quality trade-off in modern agriculture by integrating fuzzy logic with a genetic algorithm. Agriculture faces significant challenges due to climate variability, economic constraints, and the increasing demand for sustainable practices. These challenges are compounded by uncertainties and risks inherent in agricultural processes, such as fluctuating yields, unpredictable costs, and inconsistent quality. The proposed model uses a fuzzy multi-objective optimization framework to address these uncertainties, incorporating expert opinions through the alpha-cut technique. By adjusting the level of uncertainty (represented by alpha values ranging from 0 to 1), the model can shift from pessimistic to optimistic scenarios, enabling strategic decision making. The genetic algorithm improves computational efficiency, making the model scalable for large agricultural projects. A case study was conducted to optimize resource allocation for rice cultivation in Asia, barley in Europe, wheat globally, and corn in the Americas, using data from 2003 to 2025. Key datasets, including the USDA Feed Grains Database and the Global Yield Gap Atlas, provided comprehensive insights into costs, yields, and quality across regions. The results demonstrate that the model effectively balances competing objectives while accounting for risks and opportunities. Under high uncertainty (α = 0\alpha = 0α = 0), the model focuses on risk mitigation, reflecting the impact of adverse climate conditions and market volatility. On the other hand, under more stable conditions and lower market volatility conditions (α = 1\alpha = 1α = 1), the solutions prioritize efficiency and sustainability. The genetic algorithm’s rapid convergence ensures that complex problems can be solved in minutes. This research highlights the potential of combining fuzzy logic and genetic algorithms to transform modern agriculture. By addressing uncertainties and optimizing key parameters, this approach paves the way for sustainable, resilient, and productive agricultural systems, contributing to global food security. Full article

Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method. Full article

In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery machine learning/data mining (ML/DM) approach—implemented as a collection of fuzzy rule-based classifiers (FR-BCs)—characterized by a genetically optimized accuracy–interpretability trade-off. Our approach uses our generalization (showing better performance) of the well-known SPEA2 method to carry out the genetic learning and multiobjective optimization process. The main contribution of this work is designing—using our approach—a collection of fast, accurate, and interpretable FR-BCs for FDIA detection and localization from the recently published FDIA data that describe various aspects of FDIAs in smart grids. Our approach generates FDIAs’ detection and localization systems characterized by very high accuracy (97.8% and 99.5% for the IEEE 14-bus and 118-bus systems, respectively) and very high interpretability (on average, 4.6 and 3.8 simple fuzzy rules for earlier-mentioned systems, respectively, i.e., a few easy to comprehend fuzzy rules). The contribution of this paper also includes a comparative analysis of our approach and 12 alternative methods applied to the same FDIAs’ data. This analysis shows that our approach totally outperforms the alternative approaches in terms of transparency and interpretability of FDIA detection and localization decisions while remaining competitive or superior in terms of the accuracy of generated decisions. Full article

This study aims to develop an integrated framework that combines an attention-enhanced adaptive neuro-fuzzy inference system (ANFIS) with multi-objective optimization to address the challenges of subjective indicator weight allocation and insufficient nonlinear relationship modeling in environmental performance evaluation of water infrastructure. Drawing on the tri-dimensional theory of performance evaluation—success, results, and actions—the framework organizes environmental performance indicators into five primary and nine secondary dimensions. Through empirical analysis across China’s five major river basins (Yangtze, Yellow, Pearl, Huai, and Hai Rivers), our model demonstrates comprehensive superiority with faster convergence (46 iterations) and superior accuracy (R² = 0.915), significantly outperforming basic attention (62 iterations, R² = 0.862) and traditional ANFIS (85 iterations, R² = 0.828) models across all metrics. There is a significant gradient difference in environmental performance scores across the five major river basins: the Yangtze River Basin performs the best (0.99), followed by the Yellow River Basin (0.98), with the Hai River (0.92) and Huai River (0.86) in the middle, and the Pearl River Basin scoring the lowest (0.77). This disparity reflects the differences in basin characteristics and governance. Full article

In this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization to address the multi-objective control challenge. Key innovations include a self-tuning VUF mechanism that improves disturbance rejection by 42%, a weighted adaptive optimization strategy that reduces parameter tuning iterations by 37%, and an asymmetric learning factor that balances global exploration and local refinement. Benchmarks using Rastrigin, Griewank, and Sphere functions show superior convergence and 68% stability improvement. Ocean heading simulations of a 7.02 m unmanned surface vehicle (USV) using the Nomoto model show a 91.7% reduction in stabilization time, a 0.9% reduction in overshoot, and a 30% reduction in optimization iterations. The experimental validation under wind and wave disturbances shows that the heading deviation is less than 0.0392°, meeting the IMO MSC.1/Circ.1580 standard, and an 89.5% improvement in energy efficiency. Although the processing time is 12.7% longer compared to the GRO approach, this framework lays a solid foundation for ship autonomy systems, and future enhancements will focus on MPC-based time delay compensation and Field-Programmable Gate Array (FPGA) acceleration. Full article

With the rapid development of the prefabricated building industry, the green supply chain of prefabricated buildings has become a key driver of sustainable development and efficiency improvement in the industry. However, the issue of benefit distribution arising from cooperation has become the main challenge affecting the long-term stability of the supply chain. To address this, this study proposes an improved TFN-TOPSIS-Banzhaf value model, which optimizes the benefit distribution in the green supply chain of prefabricated buildings using cooperative game theory. This approach enhances both the fairness and accuracy of the distribution. The model integrates a combination of subjective and objective weighting methods based on triangular fuzzy numbers and the M-TOPSIS method for multi-factor evaluation, resulting in the corrected weight coefficients. By combining the weighting coefficients and least squares contributions, the improved Banzhaf value based on players’ weighted least squares contributions is constructed. The effectiveness and robustness of the model are verified through a case analysis, which significantly enhances the model’s ability to handle supply chain synergies and achieves a more fair and precise benefit distribution. This research provides an effective benefit distribution tool for the prefabricated building industry, promoting the continuous development of green building practices and supply chain cooperation. Full article

Accurate and objective evaluation of existing bridges is critical for ensuring the bridge’s safety and optimizing maintenance strategies. This study proposes an integrated Fuzzy Best and Worst Method and fuzzy comprehensive evaluation (FBWM-FCE) model to evaluate uncertainties in expert judgments and complex decision-making. A four-layer evaluation indicator system and five distinct grades for bridges were established, aligned with the JTG 5120-2004 and JTG/T H21-2011 standards. The FBWM innovatively employs triangular fuzzy numbers (TFNs) to reduce linguistic uncertainties and cognitive bias in bridge evaluation. Subsequently, by integrating FCE for multi-level fuzzy comprehensive operations, the method translates qualitative evaluations into quantitative evaluations using membership matrices and weights. A case study of Ding Jia Bridge and Jigongling Bridge validated the FBWM-FCE model, revealing Class III Bridge (fail condition), consistent with on-site inspections in the 2020 Bridge Inspection and Evaluation Report (Highway Administration of Hubei Provincial Department of Transportation). Comparative analysis demonstrated FBWM’s operational efficiency, requiring 20% fewer pairwise comparisons than AHP while maintaining higher consistency than BWM. The model’s reliability stems from its systematic handling of epistemic uncertainties, offering a high reduction in procedural complexity compared to standardized methods. These advancements provide a scientifically rigorous yet practical tool for bridge management, balancing computational efficiency with evaluation accuracy to support maintenance decisions. Full article

Aiming at the problem that drilling parameters are difficult to adjust in time for the driller due to the complex geological environment in underground coal mines, a drilling parameter control method based on online identification of drillability and multi-objective optimization of drilling parameters is proposed. A drillability grade identification model is established, with rotational speed and torque as input parameters, which can accurately identify the current drilling state. A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. A fuzzy PID controller is established. For the control of rotational speed parameters and drilling pressure parameters, the advantages and disadvantages of the fuzzy PID control method and the traditional PID control method are compared through simulation and experiments. A control method based on the drillability identification model and the multi-objective optimization model is established. According to the different drillability grades, the drilling parameters are adjusted in time to ensure the normal drilling state. By constantly approaching the optimal parameters through the drilling parameters, the drilling efficiency is improved. Through experimental verification, this control method effectively prevents the occurrence of drilling speed reduction and intermittent sticking and can adjust the drilling parameters to continuously optimize. Full article