This study proposes an optimization framework for selecting the Minimum Operating Strip(MOS) on wartime-damaged runways using the Harmony Search(HS) algorithm. The MOS selection problem involves multiple constraints, such as minimum runway length, repair resources, allowable damage rate, and maximum repair time. Traditional greedy methods often provide feasible solutions but fail to consider complex constraints or diverse candidate regions. By applying HS, this research demonstrates that MOS candidates can be identified more effectively, balancing repair time reduction and operational feasibility. Simulation experiments confirmed that HS improved repair time by approximately 8 ~ 15 % on average, with higher benefits under complex conditions. The results suggest that HS not only enhances computational efficiency but also strengthens decision-making support for airbase recovery operations. This study highlights the potential of metaheuristic algorithms in military engineering applications.

Abstract Mechanical design today faces critical challenges in design efficiency and multidisciplinary optimization, often constrained by high computational costs and fragmented processes. To address these issues, this paper proposes DesAgent, a multi-agent collaborative design methodology that integrates the semantic reasoning capabilities of Large Language Models (LLMs) with the numerical prediction accuracy of Reduced-Order Small Models (ROSMs). The proposed approach constructs a Semantic-Numerical Synergy Loop (SNS-Loop), enabling a closed-loop, intelligent design process that bridges semantic interpretation and numerical validation. DesAgent features a hierarchical multi-agent system consisting of four specialized agents—Requirements Analyst, Task Planner, Designer, and Feedback Evaluator—each responsible for a distinct phase of the design pipeline. The LLMs support natural language parsing and task planning, while the ROSMs ensure real-time simulation-level predictions through neural network-based surrogate models. To validate the proposed methodology, a case study on the topology optimization of a spinning frame wall plate is conducted. Experimental results show that DesAgent reduced material consumption by 21.2% while satisfying multiple constraints related to stress, deformation, and natural frequency avoidance. The entire design optimization process completed in 232 seconds, consuming only 12,044 tokens of computational resources. This work presents an efficient, low-cost, and generalizable design framework that demonstrates the feasibility of LLM-augmented collaborative intelligence in complex mechanical design tasks.

Abstract As an image-based optical technique for full-field deformation measurement, the metrological performance of three-dimensional digital image correlation (3D-DIC) highly depends on the binocular stereovision system used for acquiring stereo images of a test object surface. Such a system requires on-site adjustment to meet the specific requirements of the measurement task. However, currently existing adjustment methods rely on empirical trial-and-error during on-site setup, which not only reduces experimental efficiency but also fails to guarantee the measurement accuracy. To cope with this problem, we present a framework for optimal design of structural parameters of a 3D-DIC system composed of two cameras. Specifically, the framework utilizes the 3D reconstruction uncertainty at the field-of-view center as an objective function and optimizes the structural parameters through genetic algorithm, while considering multiple experimental constraints, including the available cameras and lenses, specimen dimensions, and experimental types. Upon implementation, the framework enables determination of structural parameters such as device specifications, stereo angle, and baseline to meet specific measurement requirements. The proposed design method was applied to three representative experimental cases, including one hypothetical and two real-world applications. Experimental verification of the two practical systems confirmed the accuracy and reliability of the design strategy. This framework enables users to efficiently develop demand-oriented 3D-DIC systems with enhanced metrological performance, offering valuable guidance for configuring 3D-DIC systems in measuring specific objects.

Invasive rats threaten island biodiversity, disrupting ecosystems and endangering native species. Although rat eradication has succeeded on many islands, tropical islands present unique management challenges. Strict regulations and financial constraints on some tropical islands further limit proven eradication methods, complicating rodent management. We applied a real-time active adaptive management approach that provided a cautious, cost-efficient, and scientifically grounded pathway to rat eradication, while adhering to strict environmental regulations, on Ilha do Meio, Brazil. The cost was US$3300 per hectare, and the management actions were grounded in close interdisciplinary collaboration. We applied rodenticide (brodifacoum), monitored the rat population, and made iterative management adjustments. The rat overpopulation was eradicated within 5 months, and population increases were observed early on in the threatened masked booby (Sula dactylatra), and the endemic Noronha elaenia (Elaenia ridleyana) and Noronha skink (Trachylepis atlantica). Despite logistical constraints, our approach proved effective and cost-efficient, marking its first application in a biological system. Our findings highlight the value of innovation, close interdisciplinary collaboration, and adaptive decision-making when the application of best-practice methods is constrained.

Distributed adaptive fault-tolerant cooperative control for fixed-wing UAVs with actuator faults and input constraints.

Offline reinforcement learning strategies guided meta-heuristics for scheduling bi-objective unmanned surface vessel problems with multiple constraints

Fault diagnosability evaluation of industrial cyber-physical systems with multiple time delays and communication constraints

3D surface displacement modeling in Lorca, Spain, using dual-orbit MT-InSAR and multiple prior constraints

تتنـاول هذه الورقة مشــكلة غير اعتيادية في حـــل مسائل نظرية الألعاب باستخـدام الطريقة البيانية، وذلك عندما تكون مصفوفة الدفع من نـوع ( 2×m ) أو ( n×2 ) ، والذي يعني أن أحد اللاعبين يمتلك إستراتيجيتين فقط كحد أقصى. تعتمد الطريقة البيانية على تمثيل استراتيجيات اللاعب ذو الإستراتيجيتين كمحورين للرسم البياني، بينما تمثل استراتيجيات اللاعب الثاني كقيود على اللاعب الأول ، حيث يتم تحديد الحل الأمثل للاعب الأول من خلال القيود التي تحدد نقطة الحل، مع استبعاد القيود التي لم تساهم في تحديد هذه النقطة. إن المشكلة التي تعالجها هذه الورقة تكمن في الحالات الخاصة التي تكون فيها نقطة الحل للاعب الأول محددة بأكثر من قيدين، وهو ما يتناقض مع الحالة الاعتيادية التي تكون فيها نقطة الحل ناتجة عن تقاطع قيدين فقط ، وهذه الحالة تعتبر حالة غير اعتيادية (حالة خاصة) ويصبح من الضروري دراسة كيفية التعامل مع هذه القيود الإضافية. تهدف الورقة إلى تحليل هذه الحالة باستخدام البرمجة الخطية، مع التركيز على كيفية تحديد القيود التي يجب استبعادها عند حل مشكلة اللاعب الثاني باستخدام الطريقة البيانية ، كما تسعى إلى دراسة تأثير استبعاد هذه القيود على كلا اللاعبين لضمان الوصول إلى الحل الأمثل الصحيح ، وتساهم هذه الورقة في فهم أعمق لآلية الحل البياني في نظرية الألعاب، خاصة في الحالات غير التقليدية التي تطرح تحديات إضافية في استخدام الطريقة البيانية . وعن طريق هذا التحليل تم معرفة القيود النشطة التي يجب استخدامها في عملية الحل بالطريقة البيانية ، والذي يعني استبعاد القيود غير النشطة واعتبارها قيود فائضة وبالتالي لا يجب استخدامها عند عملية الحل بالطريقة البيانية .

This paper aims to optimize the coordinated masking tactics of UAV smoke screen interference bombs to maximize the effective masking time against incoming missiles. The research focuses on the strategy deployment problem of UAVs delivering smoke screen interference bombs and gradually constructs and solves a series of mathematical models. First, for the single - UAV - single - bomb scenario under fixed parameters, a spatio - temporal motion trajectory model of the missile, the UAV, and the smoke screen cloud is established. Through the line - of - sight blocking principle, an effective masking duration of 1.38 seconds is calculated, which lays a benchmark for subsequent optimizations. On this basis, the problem is transformed into a multi - dimensional nonlinear optimization problem. The flight angle, speed of the UAV, and the delivery and detonation times of the interference bombs are taken as decision variables, aiming to maximize the effective masking time. By using the genetic algorithm to conduct a global parameter scan under multiple physical and tactical constraints, the effective masking time is significantly increased to 4.58 seconds. Subsequently, to address the complexity of the coordinated delivery of multiple bombs (three interference bombs) by a single UAV, the optimization model framework is extended. More decision variables are introduced, and under the delivery interval constraints, the particle swarm optimization (PSO) algorithm is used to efficiently solve the high - dimensional non - convex problem. Finally, an effective masking duration of a total of 6.25 seconds is achieved.

When solving trajectory optimization problems with no-fly zone constraints using direct collocation, both the number of constraints and the number of non-zero elements in the Jacobian matrix escalate rapidly as the number of no-fly zones increases. To address this issue, this paper presents a method for solving trajectory optimization problems with multiple no-fly zone constraints. This method consolidates multiple no-fly zone constraints into a single path constraint, thereby maintaining a consistently low level of constraints and non-zero elements in the corresponding Jacobian matrix, even as the number of no-fly zones increases. It is also proved theoretically that the Karush-Kuhn-Tucker (KKT) solution of the nonlinear programming (NLP) problems before and after the handling are equivalent. The effectiveness of the proposed method is validated through three numerical examples involving multiple no-fly zone constraints. A comparison with the ordinary method for handling no-fly zone constraints is implemented, which confirms the superiority of the proposed method in improving the solving efficiency.

The dynamics model of multi-unit virtual track trains (VTT) is inherently complex, and existing control methods face challenges in simultaneously ensuring both high-precision trajectory tracking and vehicle stability. This paper proposes a generalized modeling method for the VTT of four-unit and six-axis, along with a distributed model predictive control (MPC) strategy approach. Initially, separate models for the wheel, center of mass, and hinge plate are developed. The “placeholder method” of wheels is applied to establish the dynamics model of single unit, and the six-degree-of-freedom dynamics model of VTT is derived using the hinge plate’s “placeholder method.” Next, an MPC tracking controller is designed by analyzing the error between the desired and real-time positions of the train, which enables the train to follow the target trajectory. Under the consideration of multiple constraints, the controller calculates the optimal wheel angle for the first axle of the first unit. Finally, the optimal wheel angle for the first axle is used as input to design the MPC stability controller, and the control objective of the optimization algorithm is to minimize the error between the expected centroid sideslip angles and angular velocity of hinge plate. Simulation results under both single-line and circular curve tracking conditions demonstrate that distributed MPC strategy is able to simultaneously maintain high-precision trajectory tracking and stability of VTT. Furthermore, the proposed modeling approach exhibits excellent scalability, effectively reducing the modeling complexity and significantly improving modeling efficiency.

Abstract. With the rapid growth of the low-altitude economy, path planning for Unmanned Aerial Vehicles (UAVs) in complex urban low-altitude environments has become increasingly critical. However, urban low-altitude scenarios are influenced by buildings, meteorological conditions, regulatory restrictions and numerous factors. Traditional path planning methods struggle to effectively consider the impact of multiple constraints, making it challenging to provide effective and interpretable decision support for flight operations. This study proposes an optimized UAV flight path planning method based on an Urban Low-Altitude Navigation Knowledge Graph (ULAN-KG). Utilizing the knowledge graph, it structures the association between low-altitude flight route elements and low-altitude flight constraint factors in the urban space. The experiment selects a densely built area of Beijing for validation, where the proposed method is compared with traditional algorithms. The experimental results show that the A* algorithm improved by ULAN-KG can effectively avoid flight segments affected by strong wind conditions. When conflicting with controlled airspace events, the path planning results prioritize avoiding no-fly zones. This approach offers efficient and reliable technical support for UAV applications in complex urban low-altitude scenarios, such as logistics and emergency response.

Images captured by vision sensors in outdoor environments often suffer from haze-induced degradations, including blurred details, faded colors, and reduced visibility, which severely impair the performance of sensing and perception systems. To address this issue, we propose a haze-removal algorithm for hazy images using multiple variational constraints. Based on the classic atmospheric scattering model, a mixed variational framework is presented that incorporates three regularization terms for the transmission map and scene radiance. Concretely, an ℓp norm and an ℓ2 norm were constructed to jointly enforce the transmissions for smoothing the details and preserving the structures, and a weighted ℓ1 norm was devised to constrain the scene radiance for suppressing the noises. Furthermore, our devised weight function takes into account both the local variances and the gradients of the scene radiance, which adaptively perceives the textures and structures and controls the smoothness in the process of image restoration. To address the mixed variational model, a re-weighted least square strategy was employed to iteratively solve two separated subproblems. Finally, a gamma correction was applied to adjust the overall brightness, yielding the final recovered result. Extensive comparisons with state-of-the-art methods demonstrated that our proposed algorithm produces visually satisfactory results with a superior clarity and vibrant colors. In addition, our proposed algorithm demonstrated a superior generalization to diverse degradation scenarios, including low-light and remote sensing hazy images, and it effectively improved the performance of high-level vision tasks.

As the global energy system accelerates its transition towards high penetration of renewable energy and high penetration of power electronic devices, regional power grids have undergone profound changes in their structural forms and component composition compared to traditional power grids. Conventional dynamic equivalencing methods struggle to balance modeling accuracy and computational efficiency simultaneously. To address this challenge, this paper focuses on the dynamic equivalencing of regional power grids and proposes a dynamic equivalencing scheme considering multiple feature constraints. First, based on the structural characteristics and the evolution of dynamic attributes of regional power grids, three key constraint conditions are identified: network topology, spatial characteristics of frequency response, and nodal residual voltage levels. Secondly, a comprehensive equivalencing scheme integrating multiple constraints is designed, which specifically includes delineating the retained region through multi-objective optimization, optimizing the internal system based on coherent aggregation and the current sinks reduction (CSR) method, and constructing a grey-box external equivalent model composed of synchronous generators and composite loads to accurately fit the electrical characteristics of the external power grid. Finally, the proposed methodology is validated on a Back-to-Back VSC-HVDC-connected regional power grid in Eastern Guangdong, China. Results demonstrate that the equivalent system reproduces the original power-flow profile and short-circuit capacity with negligible deviation, while its transient signatures under both AC and DC faults exhibit high consistency with those of the reference system.

Lithium-ion batteries are a crucial technology for energy storage and cathode materials play a key role in their performance. Core-shell architectures are emerging as an effective strategy to address structural degradation and phase instability in lithium-rich cathodes such as Li2MnO3. By stabilising interfaces, these heterostructures enable improved electrochemical performance and enhanced material stability. However, practical applications are hindered by challenges in synthesising uniform structures, modelling atomic-scale behaviour at the interface and achieving a stable, coherent connection between the core and shell. To explore these interfacial challenges, we constructed Li2MnO3/Li0.69MnO core-shell structures using a custom integration framework that ensures charge neutrality, realistic separation distances and optimised atomic alignment. Molecular dynamics simulations were then employed to investigate how varying shell thicknesses (5 Å, 15 Å, and 25 Å) influence interfacial integrity and structural evolution under multiple thermodynamic constraints. Shell thickness was found to play a critical role in stabilising the interface, with thicker shells (25 Å) promoting structural coherence and reducing atomic disorder. In contrast, thinner shells introduce localised strain and lattice mismatch that can compromise interface stability. These results provide atomistic insights into how shell morphology affects structure and stability at the core-shell interface, offering valuable guidance for the rational design of durable and efficient lithium-rich cathodes for next-generation lithium-ion batteries.

ABSTRACT This study addresses key challenges in modern two-echelon supply chains, including quality management, lead time, and environmental factors. A nonlinear multi-factor lead-time reduction function is developed to better reflect real-world conditions, which is unexplored in existing literature. In addition, the impact of inspection errors on inventory decisions remains unaddressed in fuzzy environments with multiple constraints, leading to inefficiencies in handling defective products. To bridge this gap, this study incorporates two types of errors in quality inspection and models all associated supply chain costs using type-2 trapezoidal fuzzy numbers, providing a comprehensive framework for managing uncertainty. The model also considers renewable energy sources and external carbon emission factors, addressing sustainability concerns in supply chain operations. The fmincon optimization technique evaluates the model under multi-factor and traditional lead-time reduction strategies to achieve best optimal solutions. Numerical results show improved cost efficiency under the best-of-two strategy for supply chain decision-makers.

With the increasing complexity of space missions, the accuracy and efficiency of orbital maneuver planning have become crucial. This paper proposes an analytical derivation-based method for generating orbital maneuver solution sets to address the maneuver planning problem for spacecraft on-orbit services under J2 perturbation. By establishing an analytical relative motion model corrected for J2 perturbation, this method enables the rapid generation of maneuver solution sets that satisfy multiple constraints, providing diverse options for the initial mission planning phase. Simulation validation demonstrates that the method maintains good applicability across mission scenarios at different orbital altitudes. The generated solution sets not only enhance the flexibility of orbital maneuver planning but also provide a quantitative basis for optimizing the selection of mission timing windows, holding certain application value in scenarios such as space debris removal and on-orbit maintenance services.

Abstract This paper builds and extends on the authors’ previous work related to the algorithmic tool, Cylindrical Algebraic Decomposition (CAD), and one of its core applications, Real Quantifier Elimination (QE). These topics are at the heart of symbolic computation and were first implemented in computer algebra systems decades ago, but have recently received renewed interest as part of the ongoing development of SMT solvers for non-linear real arithmetic. First, we consider the use of iterated univariate resultants in traditional CAD, and how this leads to inefficiencies, especially in the case of an input with multiple equational constraints. We reproduce the workshop paper [Davenport & England, 2023], adding important clarifications to our suggestions first made there to make use of multivariate resultants in the projection phase of CAD. We then consider an alternative approach to this problem first documented in [McCallum & Brown, 2009] which redefines the actual object under construction, albeit only in the case of two equational constraints. We correct an unhelpful typo and provide a proof missing from that paper. We finish by revising the topic of how to deal with SMT or Real QE problems expressed using rational functions (as opposed to the usual polynomial ones) noting that these are often found in industrial applications. We revisit a proposal made in [Uncu, Davenport and England, 2023] for doing this in the case of satisfiability, explaining why such an approach does not trivially extend to more complicated quantification structure and giving a suitable alternative.

To address the challenges of high dimensionality, nonlinearity, and multiple constraints in distribution network fault location, where traditional intelligent optimization algorithms are prone to local optima and slow convergence, this paper proposes a fault location method based on the Archimedes Optimization Algorithm (AOA). By constructing a fault state encoding model for the distribution network, the fault location problem is transformed into a binary optimization problem. Leveraging the global search capability and convergence characteristics of the AOA, rapid and accurate location of faulty sections is achieved. Simulation experiments based on the IEEE 33-node system under various fault scenarios, including single-point and multi-point faults, demonstrate that the proposed method outperforms comparative algorithms in terms of convergence speed.