Search Results (68)

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

The assembly optimization design of satellite components is a crucial element in the overall design of satellites. In this paper, a novel three-dimensional assembly optimization design problem (3D-AODP) for multi-module micro–nano satellite components is proposed according to the engineering requirements, aiming at optimizing the satellite mass characteristics, and taking into account constraints such as space interference, space occupation and special location. Multi-module micro–nano satellites are a new type of satellite configuration based on the assembly of multiple U-shaped cube units. The 3D-AODP of its components is a challenging two-layer composite optimization task involving discrete variable optimization of component allocation and continuous variable optimization of component layout, which interact with each other. To solve the problem, a hybrid assembly optimization method based on tabu search (TS) and multi-objective differential evolutionary (MODE) algorithms is proposed, in which the assignment problem of the components is converted into a domain search problem by the TS algorithm. The space interference constraints and space occupancy constraints of the components are considered, and an assignment scheme with the minimum mass difference is obtained. On this basis, a bi-objective differential evolutionary algorithm is used to develop the layout optimization problem for the components, which takes into account the spatial non-interference constraints and special location constraints of the components, and obtains the Pareto solution set of the assembly scheme under the optimal mass characteristics (moment of inertia and product of inertia). Finally, the feasibility and effectiveness of the proposed method is demonstrated by an engineering case. Full article

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques. Full article

AutoML is enabling many practitioners to use sophisticated Machine Learning pipelines even without being experienced in building application-specific solutions. Adapting AutoML to the field of unsupervised learning, particularly to the task of clustering, is challenging, as clustering is highly subjective and application-specific; the goal is not to find the best way to group data objects based on previously seen examples, but to find interesting new structures within potentially unknown data objects that provide actionable insights. The level of interestingness of a clustering is highly subjective and is subject to a variety of different characteristics making different clusterings of the same dataset (e.g., grouping people by age, gender, or special interests). In this paper, we propose an Automated Exploratory Clustering framework which determines multiple clusterings satisfying different notions of interestingness automatically. To this end, we generate multiple clusterings via AutoML processes and return a selection of clusterings, from which the user can explore the most preferred ones. We use different methods like the skyline operator to prune non-Pareto-optimal clusterings wrt. different dimensions of interestingsness and deliver a small set of valuable clusterings. In this way, our approach enables practitioners as well as domain experts to identify valuable clusterings without becoming experts in clustering as well, thus reducing human efforts and resources in finding application-specific solutions. Our empirical investigation with current state-of-the-art methods is carried out on a number of benchmark datasets, where a well-established ground truth can proxy for the wishes of a domain expert and multiple interestingness properties of the clusterings. Full article

Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize energy consumption and computational time. New mathematical models for DCS performance and reliability are proposed, based on a mass service system framework, along with a multi-criteria optimization model designed for resource-intensive computational problems. This model employs a multi-criteria GA to generate a diverse set of Pareto-optimal solutions. Additionally, a decision-support system is developed, incorporating the multi-criteria GA, allowing for customization of the genetic algorithm (GA) and the construction of specialized ANNs for specific problem domains. The application of the decision-support system (DSS) demonstrated performance of 1220.745 TFLOPS and an availability factor of 99.03%. These findings highlight the potential of the proposed DCS framework to enhance computational efficiency in relevant applications. Full article

In the context of expensive constraint multi-objective problems, it is evident that the feasible domain shapes and sizes of different problems vary considerably. The difficulty in finding optimal solutions presents a significant challenge in ensuring the surrogate-assisted evolutionary algorithm’s feasibility, convergence, and diversity. To more effectively address the distinctive characteristics of the feasible domain and objective function across a range of problems, we have developed a Kriging-based surrogate-assisted evolutionary algorithm tailored to the current population’s preferences. The algorithm can optimize the population according to the current population’s requirements. Additionally, considering the varying degrees of accuracy observed in the surrogate models at different stages, this paper employs a dynamic approach to the number of surrogate model evaluations, contingent on the accuracy of the current surrogate model. Two types of Pareto frontier search are distinguished: unconstrained and constrained. Moreover, distinct fill sampling strategies are devised in accordance with the specific optimization requirements of the current population. After assessing the proposed solutions, the discrepancy between the actual fitness value and the surrogate model’s prediction is calculated.The discrepancy is used to modify the number of evaluations conducted on the surrogate model. In order to illustrate the algorithm’s efficacy, it is benchmarked against the current state-of-the-art algorithms on various test problems. The experimental results demonstrate that the proposed algorithm performs better than other advanced methods. Full article

In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV’s endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization’s effectiveness in minimizing the total inspection distance and balancing UAV workloads. Full article

With the popularization of internet of things (IoT), the energy consumption of mobile edge computing (MEC) servers is also on the rise. Some important IoT applications, such as autonomous driving, smart manufacturing, and smart wearables, have high real-time requirements, making it imperative for edge computing to reduce task response latency. Virtual machine (VM) placement can effectively reduce the response latency of VM requests and the energy consumption of MEC servers. However, the existing work does not consider the selection of weighting coefficients for the optimization objectives and the feasibility of the solution. Besides, these algorithms scalarize the objective functions without considering the order-of-magnitude difference between objectives. To overcome the above problems, the article proposes an algorithm called EVMPRL for VM placement in edge computing based on reinforcement learning (RL). Our aim is to find the Pareto approximate solution set that achieves the trade-off between the response latency of VM requests and the energy consumption of MEC servers. EVMPRL is based on the Chebyshev scalarization function, which is able to efficiently solve the problem of selecting weighting coefficients for objectives. EVMPRL can always search for solutions in the feasible domain, which can be guaranteed by selecting the servers that can satisfy the current VM request as the next action. Furthermore, EVMPRL scalarizes the Q-values instead of the objective functions, thus avoiding the problem in previous work where the order-of-magnitude difference between the optimization objectives makes the impact of an objective function on the final result too small. Finally, we conduct experiments to prove that EVMPRL is superior to the state-of-the-art algorithm in terms of objectives and the solution set quality. Full article

Background: Nanotechnology has been the main area of focus for research in different disciplines, such as medicine, engineering, and applied sciences. Therefore, enormous efforts have been made to insert the use of nanoparticles into the daily routines of different platforms due to their impressive performance and the huge potential they could offer. Among numerous types of nanomaterials, titanate nanotubes have been widely recognised as some of the most promising nanocarriers due to their outstanding profile and brilliant design. Their implementation in pharmaceutical applications is of huge interest nowadays as it could be of fundamental importance in the development of the pharmaceutical industry and therapeutic systems. Methods: In the present work, a risk assessment-based procedure was developed and completed using ANN-based modeling to enable the design and fabrication of titanate nanotube-based drug delivery systems with desired properties, based on the critical analysis and evaluation of data collected from published articles regarding titanate nanotube preparation using the hydrothermal treatment method. Results: This analysis is presented as an integrated pathway for titanate nanotube preparation and utilization in a proper way that meets the strict requirements of pharmaceutical systems (quality, safety, and efficacy). Furthermore, a reasonable estimation of the factors affecting titanate nanotube preparation and transformation from traditional uses to novel pharmaceutical ones was established with the aid of a quality by design approach and risk assessment tools, mainly an Ishikawa diagram, a risk estimation matrix, and Pareto analysis. Conclusions: To the best of our knowledge, this is the first article using the QbD approach to suggest a systematic method for the purpose of upgrading TNT use to the pharmaceutical domain. Full article

Delivering persistent values in a dynamic environment is a challenging but imperative capability for a system-of-systems (SoS). Practitioners in the SoS and defense domains are exploring the benefits of the operational-level reconfiguration strategies via new operational concepts such as mosaic warfare. However, an architecture design that allows reconfiguration is also a crucial task, but has not yet received adequate attention, not to mention accounting for the mutual impact between architecture design alternatives and reconfiguration options. Therefore, this paper proposes an integrated method that can select the architecture with a specific inherent structure in the design phase that supports dynamic reconfiguration during the operational phase. This method firstly builds a structural framework that connects architecture design and reconfiguration, and identifies the enablers for SoS architecture reconfiguration. After developing an SoS effectiveness evaluator, the method constructs an integrated multi-objective formulation for the initial architecture selection and reconfiguration process, and provides a solution algorithm based on a fast non-dominated sorting genetic algorithm. An application to an air and missile defense SoS illustrates the effectiveness of the proposed method. The generated Pareto optimal set of solutions that have non-dominated recoverability and survivability provide useful decision support for SoS composition and initial architecture configuration, based upon which an SoS can also respond effectively to disruptions by computing the reconfiguration decisions. Full article

Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment as soon as possible. In particular, the introduction of transfer learning in DMOAs has led to good results in solving DMOPs. However, the selection of valuable historical knowledge and the mitigation of negative transfer remain important problems in existing transfer learning-based DMOAs. Method: A DMOA based on multi-environment knowledge selection and transfer (MST-DMOA) is proposed in this article. First, by clustering historical Pareto optima, some representative solutions that can reflect the main evolutionary information are selected as knowledge of the environment. Second, the similarity between the historical and current environments is evaluated, and then the knowledge of multiple similar environments is selected as valuable historical knowledge to construct the source domain. Third, solutions with high quality in the new environment are obtained to form the target domain, which can better help historical knowledge to adapt to the current environment, thus effectively alleviating negative transfer. Conclusions: We compare the proposed MST-DMOA with five state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results verify the excellent performance of MST-DMOA in solving DMOPs. Full article

As the development of floating offshore wind turbines (FOWTs) progresses from offshore to deeper sea, the demands on mooring systems to ensure the safety of the structure have become increasingly stringent, leading to a concomitant rise in costs. A parameter optimization method for the mooring system of FOWTs is proposed, with the mooring line length and anchor radial spacing as the optimization variables, and the minimization of surge, yaw, and nacelle acceleration as the objectives. A series of mooring system configuration samples are generated by the fully analytical factorial design method, and the open source program OpenFAST is employed to simulate the global responses in the time domain. To enhance the efficiency of the optimization process, a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), is utilized to find the Pareto-optimal solutions, alongside a Kriging model, which serves as a surrogate model for the FOWTs. This approach was applied to an IEC 15MW FOWT to demonstrate the optimization procedure. The results indicate that the integration of the genetic algorithm and the surrogate model achieved rapid convergence and high accuracy. Through this optimization process, the longitudinal motion response of FOWTs is reduced by a maximum of 6.46%, the yaw motion by 2.87%, and the nacelle acceleration by 11.55%. Full article

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements. Full article

We establish a relationship between stochastic differential games (SDGs) and a unified forward–backward coupled stochastic partial differential equation (SPDE) with discontinuous Lévy Jumps. The SDGs have q players and are driven by a general-dimensional vector Lévy process. By establishing a vector-form Ito-Ventzell formula and a 4-tuple vector-field solution to the unified SPDE, we obtain a Pareto optimal Nash equilibrium policy process or a saddle point policy process to the SDG in a non-zero-sum or zero-sum sense. The unified SPDE is in both a general-dimensional vector form and forward–backward coupling manner. The partial differential operators in its drift, diffusion, and jump coefficients are in time-variable and position parameters over a domain. Since the unified SPDE is of general nonlinearity and a general high order, we extend our recent study from the existing Brownian motion (BM)-driven backward case to a general Lévy-driven forward–backward coupled case. In doing so, we construct a new topological space to support the proof of the existence and uniqueness of an adapted solution of the unified SPDE, which is in a 4-tuple strong sense. The construction of the topological space is through constructing a set of topological spaces associated with a set of exponents $\{{\gamma }_1,{\gamma }_2,\dots \}$ under a set of general localized conditions, which is significantly different from the construction of the single exponent case. Furthermore, due to the coupling from the forward SPDE and the involvement of the discontinuous Lévy jumps, our study is also significantly different from the BM-driven backward case. The coupling between forward and backward SPDEs essentially corresponds to the interaction between noise encoding and noise decoding in the current hot diffusion transformer model for generative AI. Full article

Traditional integer-order admittance control is widely used in industrial scenarios requiring force control, but integer-order models often struggle to accurately depict fractional-order-controlled objects, leading to precision bottlenecks in the field of precision machining. For robotic precision polishing scenarios, to enhance the stability of the control process, we propose a more physically accurate five-parameter fractional-order admittance control model. To reduce contact impact, we introduce a method combining the rear fastest tracking differential with fractional-order admittance control. The optimal parameter identification for the fractional-order system is completed through Pareto optimality and a time–frequency domain fusion analysis of the control system. We completed the optimal parameter identification in a simulation, which is applied to the robotic precision polishing scenario. This method significantly enhanced the force control precision, reducing the error margin from 15% to 5%. Full article