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In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in Machine Learning (ML) and Deep Learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers. [ABSTRACT FROM AUTHOR]

As the EU and the US successively proposed the carbon border adjustment mechanism (CBAM) and the Clean Competition Act (CCA), importers are now required to declare the carbon emissions of their products. Particularly, wheel companies face increasing time pressure with the gradual development of implementation paths for net-zero emissions and implementing carbon pricing policies in various countries. This research uses the production data of Taiwan wheel companies to establish a mathematical model for the activity-based cost optimisation decision-making process. It explores the impact of different carbon emission costs on a company's product mix with pricing decisions. Further, the research proposes five mathematical programmes for single-period cost optimisation decision-making under carbon pricing. These models provide some detailed view, such as changes in profits and product mix caused by high carbon taxes or other changes for each resource. Furthermore, the production cost optimal model proposed in this research can be used by enterprises to fulfill their social responsibilities while ensuring profitability and serve as a reference for making net-zero transformation decisions. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Libcint is a library designed for the evaluation of analytical integrals for Gaussian type orbitals. It prioritizes simplicity, ease of use, and efficiency for the development of quantum chemistry programs. In the release of version 6.0, Libcint supports the computation of integrals for various operators, such as overlap, Coulomb, Gaunt, Breit, attenuated Coulomb, Slater-type geminals, and Yukawa potential, as well as arbitrary orders of derivatives for these operators. To enhance the usability of the library, Libcint provides a uniform function signature for all integral functions. A code generator is included to automate the implementation of new integrals. To achieve better performance on modern central processing unit architectures, the library employs explicit single instruction multiple data parallelization in the code implementation. [ABSTRACT FROM AUTHOR]

This work describes optimizations of N-electron system wave functions by means of the simulated annealing technique within the doubly occupied configuration interaction framework. Using that technique, we minimize the energy variance of a Hamiltonian, providing determinations of wave functions corresponding to ground or excited states in an identical manner. The procedure that allows us to determine electronic spectra can be performed using treatments of restricted or unrestricted types. The results found in selected systems, described in terms of energy, spin, and wave function, are analyzed, showing the performance of each method. We also compare these results with those arising from more traditional approaches that minimize the energy, in both restricted and unrestricted versions, and with those obtained from the full configuration interaction treatment. [ABSTRACT FROM AUTHOR]

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment. [ABSTRACT FROM AUTHOR]

This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

The notion ‘Convexity’ is applied in various areas of mathematics particularly in optimization techniques. It is known that this concept is applied in fuzzy sets, which is studied by many authors. This article deals with convexity utilized for neutrosophic and interval valued neutrosophic sets which is a generalization of intuitionistic fuzzy sets. Also some interesting preservation properties of convexity under intersection operators in interval valued neutrosophic sets are discussed. Adding to the discussion, preservation property of digital convexity under intersection using deformation and other techniques are touched upon. Eventually the application of convexity to decision making are illustrated using examples. [ABSTRACT FROM AUTHOR]

In recent times, there has been a notable surge in the utilization of Internet of Things (IoT) network devices due to their vast applications. However, this rapid growth has undoubtedly led to raised energy consumption, which, in turn, has raised significant concerns about the environment. Consequently, there is a growing demand for green computing techniques that can mitigate IoT device's energy usage and carbon footprint. Clustering IoT networks is a useful strategy for extending their lifespan. However, clustering presents a complex optimization problem that falls under the category of NP‐hard; hence making it a challenging issue. Nevertheless, using meta‐heuristics algorithms has greatly improved our ability to tackle such challenges. Therefore, this study introduces a clustering scheme called EQ‐AHA, which combines Equilibrium optimization and artificial hummingbird optimization techniques to enhance the efficiency of IoT‐based Software‐Defined Wireless Sensor Networks (IoT‐SDWSN). The primary goal of EQ‐AHA is to select the Cluster Heads (CHs) and determine the optimal path between CHs and the Base Station (BS). EQ‐AHA employs a fitness function that considers three important factors: the distance between CHs, the distance between nodes and the CHs, and the energy levels of the nodes. Overall, this strategy improves the network's performance by 31.6% compared to other State‐of‐the‐Art (SoA) algorithms. [ABSTRACT FROM AUTHOR]

Complex websites comprise a variety of diverse web entities, which require constant restructuring resonating with the latest trends, shifting consumer expectations and market driven changes. Therefore, designing suitable models to optimally restructure such websites is of paramount importance and must take into consideration several factors about the web entities such as display size, download time, type, location in the page, sales likelihood, discounts, and the ongoing trend. A recent study has taken all these attributes into consideration and designed a model based on the Access Score, Interface Score, and Purchase Score. However, this model suffers from certain drawbacks such as it did not address the underlying cohesiveness between these attributes. Further, it provided a single optimal solution to the adaptive website structure optimization (AWSO) problem and relied on the a priori knowledge of weights. The basis of the new proposed model is that there can be more than one optimal solution to the AWSO problem in the real world. The novel tri‐objective optimization model uses NSGA‐II algorithm to simultaneously optimize the attributes and finds advantageous trade‐off solutions without requiring a priori knowledge of weights. The proposed MO‐AWSONSGA‐II model is shown to outperform the existing model proving it better suited for the AWSO problem. [ABSTRACT FROM AUTHOR]

This study presents a novel approach of intelligent parameter identification (IPI) for a high‐cycle accumulation (HCA) model of sand, which reduces the subjective errors on manual parameter calibration and makes the use of the HCA model more accessible. The technique is based on optimization theory and adopts the cuckoo search algorithm (CSA). To improve search ability and convergence speed of CSA, several enhancements are implemented. First, the improved CSA (ICSA) incorporates quasi‐opposition learning to expand the search space and replaces the original search strategy with a Cauchy random walk to enhance global search ability. Second, an adaptive scaling factor is introduced in the algorithm's control parameters to achieve a better balance between exploration speed and accuracy. Third, a dynamic inertia weight is used to balance the search between global and local spaces when generating new nest positions after abandoning old ones. The performance of the ICSA‐based IPI approach is evaluated by comparing it with the original CSA‐based IPI and manual calibration in determining the HCA model parameters. A comprehensive analysis is also conducted to assess the effectiveness and superiority of each improvement strategy introduced in the ICSA over the original CSA. All comparisons demonstrate that the proposed ICSA‐based IPI method is more powerful and efficient in finding optimal parameters. [ABSTRACT FROM AUTHOR]

MEC (Multi-access Edge Computing) is vital in 5G and beyond (B5G) for reducing latency and enhancing network efficiency through local processing, crucial for real-time applications and improved security. This drives the adoption of advanced architectures like Fog Radio Access Network (F-RAN), which uses distributed resources from Radio Resource Heads (RRHs) or fog nodes to enable parallel computation. Each user equipment (UE) task can be processed by RRHs, fog access points, cloud servers, or the UE itself, depending on resource capacities. We propose MoNoR, a centralized approach for optimal task processing in F-RAN. MoNoR optimizes the selection of offloading modes, assignment of tasks to computation nodes, and allocation of radio resources using global network information. Given the computational complexity of this endeavor, we employ an evolutionary optimization technique rooted in Genetic Algorithms to address the problem efficiently. Simulations show MoNoR's superiority in minimizing latency over previous F-RAN offloading strategies. [ABSTRACT FROM AUTHOR]

Accurate channel estimation poses a significant challenge in the reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) wireless communication system. The fully passive nature of the RIS primarily relies on cascaded channel estimation, given its limitation in transmitting and receiving signals. Although the advantageous of this approach, the increase in the number of RIS elements leads to an exponential growth in the channel coefficient, resulting in costly pilot overhead. To address this challenge, the paper proposes a two-phase framework for separate channel estimation. The framework involves incorporating a few active elements within the passive RIS, enabling the reception and processing of pilot signals at the RIS. Through leveraging the difference in coherence time of the channel, the estimation of the time-varying channel among user equipment (UE) and RIS, as well as the estimation of the pseudo-static channel among RIS and base station (BS), can be performed separately. The two-phase separate channel estimation framework operates as follows: In the first phase, the BS-RIS channel is estimated at the RIS through the utilization of the few active elements. An iterative weighting methodology is employed to formulate the mathematical optimization problem for estimating the BS-RIS signal model. Subsequently, a proposed algorithm grounded on gradient descent (GD) is introduced to efficiently address and solve the optimization problem. In the second phase, the estimation of the UE-RIS channel is achieved by transforming the signal model of the received channel into an analogous tensor model known as Parallel Factor (PARAFAC). This transformation is followed by the application of the least squares (LS) algorithm within this tensor-based representation at BS. The effectiveness of the proposed framework is demonstrated through simulation findings, considering minimum pilot overhead, average spectral efficiency, and normalized mean square error (NMSE). A comparative analysis is performed with three other state-of-the-art existing schemes. [ABSTRACT FROM AUTHOR]

As global economies seek to transition to low-carbon energy systems to achieve net zero targets, hydrogen has potential to play a key role to decarbonise sectors that are unsuited to electrification or where long-term energy storage is required. Hydrogen can also assist in enabling decentralized renewable power generation to satisfy higher electricity demand to match the scale-up of electrified technologies. In this context, suitable transport, storage, and distribution networks will be essential to connect hydrogen generation and utilisation sites. This paper presents a techno-economic impact evaluation of international marine hydrogen transportation between Canada and the Netherlands, comparing liquid hydrogen, ammonia, and a dibenzyl toluene liquid organic hydrogen carrier (LOHC) as potential transport vectors. Economic costs, energy consumption and losses in each phase of the transportation system were analysed for each vector. Based on the devised scenarios, our model suggests levelised costs of hydrogen of 6.35–9.49 $ 2022 /kg H2 and pathway efficiencies of 55.6–71.9%. While liquid hydrogen was identified as the most cost-competitive carrier, sensitivity analysis revealed a merit order for system optimisation strategies, based upon which LOHC could outperform both liquid hydrogen and ammonia in the future. [Display omitted] • Comparative techno-economic analysis of large-scale, transatlantic hydrogen transport technologies. • Model suggests levelised costs of hydrogen of 6.35–9.49 $/kg and pathway efficiencies of 55.6–71.9%. • Liquid hydrogen was identified as the most suitable carrier for Canada-EU hydrogen shipping. • Improved dehydrogenation efficiencies could make LOHCs cost competitive. • Co-locating reconversion plants with industrial heat sources should be a priority for deployment. [ABSTRACT FROM AUTHOR]

Metaheuristic methods are optimization techniques generally based on natural or physical phenomena. Although these processes inspire, the underlying mechanisms of these phenomena still need to be fully understood. Translating these mechanisms into algorithmic solutions may lead to unclear strategies, whose performance can exhibit unpredictable behavior. On the other hand, a radial basis function neural network (RBFNN) is a powerful method for approximating highly complex functions through a training process. Its key advantage lies in its interpretable structure, which allows us to understand the underlying relationships among the input–output data. This paper proposes a new metaheuristic algorithm based on the architecture of an RBFNN. Under this approach, an RBFNN is trained to approximate the objective function based on initial input data distributed from the search space. After training, the radial basis functions and weights associated with the neurons that maintain a strong influence or contribution to obtain the highest values of the cost function are determined. The parameters of these radial basis functions define the promissory regions of the input space that are used to produce the individuals of the new population through the Latin hypercube sampling technique. Therefore, during the optimization process, different regions of the search space represented by radial basis functions are explored and exploited as the accuracy of the cost function approximation increases. The experimental results demonstrated the success of our approach, as it outperformed the established metaheuristic algorithms on various benchmark functions. [ABSTRACT FROM AUTHOR]

Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings. [ABSTRACT FROM AUTHOR]

Optimizing the last train schedule is challenged by the elastic nature of passenger flow and the immutability of the schedule once announced to the public. The elastic passenger demand is first mathematically described, and an optimization method is proposed to minimize the adjustment to the last trains while maximizing the accessible passengers within the metro network. Linearization techniques are then employed to transform it into a linear programming form. The proposed method is verified through a real-world case study, which indicate an 8.4% increase in accessible passenger flow on a weekday with adjustments required for only 3 lines and 10 stations’ last train times. In a special event scenario, a 9.8% is achieved with adjustments to 2 lines and 8 stations’ last train times. The results suggest that for existing metro systems, judiciously adjusting the last train times can effectively enhance the overall level of nighttime operational service. [ABSTRACT FROM AUTHOR]

The integrated energy microgrid (IEM) has good potential for both active and reactive power regulation, which are gradually becoming critical resources for participating in power system ancillary services. This paper constructs a distributed optimization model for the IEMs participating in voltage regulation ancillary services by considering multiple physical network constraints. To reduce computational complexity, the DistFlow power flow model and the second‐order cone relaxation method are introduced to transform the optimization model into a second‐order cone programming problem. Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. Finally, the proposed optimization model and solution method are tested in the modified IEEE‐33 node test case to verify their effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and sparse data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such problems. HOSS operates by breaking down extensive systems models into individual pathway blocks organized in a nested hierarchy. At the first level, dependencies are solely on signalling inputs, and subsequent levels rely only on the preceding ones. We demonstrate that each independent pathway in every level can be efficiently optimized. Once optimized, its parameters are held constant while the pathway serves as input for succeeding levels. We develop an algorithmic approach to identify the necessary nested hierarchies for the application of HOSS in any given biochemical network. Furthermore, we devise two parallelizable variants that generate numerous model instances using stochastic scrambling of parameters during initial and intermediate stages of optimization. Our results indicate that these variants produce superior models and offer an estimate of solution degeneracy. Additionally, we showcase the effectiveness of the optimization methods for both abstracted, event-based simulations and ODE-based models. Author summary: Biochemical pathway models integrate quantitative and qualitative data to understand cell functioning, disease effects, and to test treatments in silico. Constructing and optimizing these models is challenging due to the complexity and multitude of variables and parameters involved. Although hundreds of biochemical models have been developed and are available in repositories, they are rarely reused. To enhance the utilization of these models in biomedicine, we propose HOSS, an innovative hierarchical model optimization method. HOSS takes advantage of the modular structure of pathway models by breaking down large mechanistic computational models into smaller modules. These modules are then optimized progressively, starting with input modules and following causality paths. This method significantly reduces the computational burden as each step involves solving a simpler problem. By making the optimization process more manageable, HOSS accelerates the lifecycle of biochemical models and promotes their broader use in biomedical research and applications. [ABSTRACT FROM AUTHOR]

To cope with the uncertainty brought by the large-scale integration of renewable energy under the goal of carbon neutrality, it is necessary to tap and utilize flexible and adjustable resources from both the source and the load side at the same time. Hence, a flexible low-carbon optimal scheduling method for distribution networks is proposed in this paper, which takes into account the participation of heat storage industrial loads in demand response. Firstly, the model of the gas turbine equipped with a flexible carbon capture device is established, and the non-convex constraint introduced by the adjustable flue gas diversion ratio is convexified. Then the model of the fused magnesium load, a representative of heat storage industrial loads, is established for its participation in demand response. The segment linearization and convexification methods are performed on the conditional productivity constraints of the fused magnesium load. On this basis, a mixed-integer linear programming model for flexible and low-carbon optimal dispatch of the distribution network is developed by using the stochastic optimization theory and solved by commercial solvers. The proposed method is verified to be able to ensure the economic operation of the distribution network while reducing carbon emissions and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

To elucidate the application of HF approaches within urology to improve clinical work-system function via a systematic scoping review. Human Factors (HF) plays an integral role to improving safety, efficiency, and well-being by optimizing work-system interactions. Despite its established application across various high-risk industries, the systematic exploration of HF methods applied within urology remains limited. A scoping review of HF interventions implemented within urologic care from 1980 to 2023 was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Web of Science, PubMed, and OVID Medline databases were searched. Database searching resulted in 10,850 articles, after systematic review, 22 studies were included. The included studies predominantly originated from the United States and covered diverse areas of urology, including surgical procedures, diagnostics, and patient care. Interventions varied widely from ergonomic equipment implementations to modifications in clinical processes and team dynamics. Most studies utilized predetermined interventions based on prior literature or experiential anecdotes (15, 65.22%), while a smaller subset employed data-driven strategies to tailor interventions (8, 34.7%). The HF methods employed included questionnaires, retrospective reviews, observations, and physical measurements, targeting improvements in patient experiences, operational efficiencies, and clinical outcomes. This review underscores the emergent role of HF in urology, highlighting a broad spectrum of interventions and methodological approaches that contribute to system optimization. Future endeavors should focus on collaborative efforts to develop standardized HF applications in urology, promoting a safer, more efficient, and clinician-friendly environment. [ABSTRACT FROM AUTHOR]

In this tutorial review paper we explain the concept of homomorphism and identify some principles that justify homomorphism construction based on the homogeneity of structure and coupling in systems with multiple components. We discuss some simple examples to show how these underlying justifying conditions can arise. Examples include brain simulation, combat attrition, and the greatly reduced computational complexity represented by Pascal's triangle. Homomorphism is also shown to be fundamental for constructing approximate low-resolution models. Models that simplify complex time-demanding simulation models are often used as surrogate or metamodels in system optimization. However, such models are fitted typically to computationally derived response surfaces and not structurally related directly to the originals using homomorphisms as described here. Along these lines, we show how homomorphism plays an essential role in a novel approach being developed to strongly control tree expansion in state space explorations of stochastic system simulation. [ABSTRACT FROM AUTHOR]

This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method's primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of overfitting and maintaining biological realism in deep learning algorithms (DLAs), for predicting individual tree taper using stem diameters outside bark (DOB) and total tree volume (TTV). To this end, DLAs were trained using two different approaches: a "hyperparameter-optimized DLA", which customizes specific hyperparameters such as learning rate and momentum rate, and a "regularization-optimized DLA", which incorporates optimization techniques like early stopping with root mean square error, L1 and L2 regularization, and dropout. Although obtaining the deterioration in predictive capabilities statistics from the taring dataset to the validation dataset by standard DLA with adaptive learning processes without customizing the hyperparameters and regularization parameters, the hyperparameter-optimized DLA with a momentum of 0.8, and a 7 # hidden layer for the TTV and regularization-optimized DLA with a dropout ratio of 0.000001, a 3 # hidden layer for the DOB demonstrated comparable predictive capabilities statistics across both training and validation datasets with generating biologically plausible predictions. Our results support that these hyperparameter-optimized and regularization-optimized DLAs, by improving the "black-box" nature of artificial intelligence, offer significant potential for enhanced interpretability and performance by improving the problem of overfitting and the violations biological realism in forest biometrics applications. [ABSTRACT FROM AUTHOR]

Robots are attracing increasing attention in the machining field owing to their great flexibility, vast workspace and low cost. Functional redundancy exists when robots are engaged in milling tasks. Redundancy resolution be considered not only in milling robot motion planning, but also in workpiece layout. A two-step motion planning method is proposed to optimize the trajectory and the workpiece set-up simultaneously to improve planning efficiency with less computational burden. In the first stage, a bilevel optimization technique is proposed that can plan the motion from a global perspective and search the optimal workpiece layout. In the second step, based on a novel smoothness index, a modified whale optimization algorithm combined with the differential evolution algorithm (DE-WOA) is proposed to resolve redundancy under robot and machining error constraints. Experiments demonstrate that the proposed method can enhance the quality of surface machining while avoiding the impact of path length on computational complexity. [ABSTRACT FROM AUTHOR]

Finding reasonably good solutions using a fewer number of objective function evaluations has long been recognized as a good attribute of an optimization algorithm. This becomes more important, especially when dealing with very high-dimensional optimization problems, since contemporary algorithms often need a high number of iterations to converge. Furthermore, the excessive computational effort required to handle the large number of design variables involved in the optimization of large-scale steel double-layer grids with complex configurations is perceived as the main challenge for contemporary structural optimization techniques. This paper aims to enhance the convergence properties of the standard guided stochastic search (GSS) algorithm to handle computationally expensive and very high-dimensional optimization problems of steel double-layer grids. To this end, a repair deceleration mechanism (RDM) is proposed, and its efficiency is evaluated through challenging test examples of steel double-layer grids. First, parameter tuning based on rigorous analyses of two preliminary test instances is performed. Next, the usefulness of the proposed RDM is further investigated through two very high-dimensional instances of steel double-layer grids, namely a 21,212-member free-form double-layer grid, and a 25,514-member double-layer multi-dome, with 21,212 and 25,514 design variables, respectively. The obtained numerical results indicate that the proposed RDM can significantly enhance the convergence rate of the GSS algorithm, rendering it an efficient tool to handle very high-dimensional sizing optimization problems. [ABSTRACT FROM AUTHOR]

The global minimum point of an optimization problem is of interest in engineering fields and it is difficult to solve, especially for a nonconvex large-scale optimization problem. In this article, we consider a new memetic algorithm for this problem. That is to say, we use the continuation Newton method with the deflation technique to find multiple stationary points of the objective function and use those found stationary points as the initial seeds of the evolutionary algorithm, other than the random initial seeds of the known evolutionary algorithms. Meanwhile, in order to retain the usability of the derivative-free method and the fast convergence of the gradient-based method, we use the automatic differentiation technique to compute the gradient and replace the Hessian matrix with its finite difference approximation. According to our numerical experiments, this new algorithm works well for unconstrained optimization problems and finds their global minima efficiently, in comparison to the other representative global optimization methods such as the multi-start methods (the built-in subroutine GlobalSearch.m of MATLAB R2021b, GLODS, and VRBBO), the branch-and-bound method (Couenne, a state-of-the-art open-source solver for mixed integer nonlinear programming problems), and the derivative-free algorithms (CMA-ES and MCS). [ABSTRACT FROM AUTHOR]

In this study, the stiffener geometry was optimized using curvilinear 3D printing to enhance the buckling resistance. A bio-inspired skin/stiffener composite that mimicked spider-web structures was generated. A dataset was formulated for the regression analysis, covering buckling stresses under distinct feature values. The regression equations, crafted using a deep neural network trained on the dataset, were evaluated. The derived regression equation was subjected to sequential quadratic programming, a mathematical optimization, to determine the optimal value of the explanatory variable. This was aimed at maximizing the buckling stress-to-stiffener volume ratio, which is the objective variable. The optimized arrangement exhibited significantly improved buckling resistance, with approximately 163% higher buckling stress than conventionally designed structures with straight stiffeners of similar weight. [ABSTRACT FROM AUTHOR]

Two aspects of neural networks that have been extensively studied in the recent literature are their function approximation properties and their training by gradient descent or gradient flow. The approximation problem seeks accurate approximations with a minimal number of weights. In most of the current literature these weights are fully or partially hand-crafted, showing the capabilities of neural networks but not necessarily their practical performance. In contrast, optimization theory for neural networks heavily relies on an abundance of weights in over-parametrized regimes. This paper balances these two demands and provides an approximation result for shallow networks in 1d with non-convex weight optimization by gradient flow. We consider finite width networks and infinite sample limits, which is the typical setup in approximation theory. Technically, this problem is not over-parametrized, however, some form of redundancy reappears as a loss in approximation rate compared to best possible rates. [ABSTRACT FROM AUTHOR]

A numerical optimization technique used to address nonlinear programming problems is the trust region (TR) method. TR uses a quadratic model, which may represent the function adequately, to create a neighbourhood around the current best solution as a trust region in each step, rather than searching for the original function's objective solution. This allows the method to determine the next local optimum. The TR technique has been utilized by numerous researchers to tackle multi‐objective optimization problems (MOOPs). But there is not any publication that discusses the issue of applying a chaotic search (CS) with the TR algorithm for solving multi‐objective (MO) problems. From this motivation, the main contribution of this study is to introduce trust‐region (TR) technique based on chaotic search (CS) for solving MOOPs. First, the reference point interactive approach is used to convert MOOP to a single objective optimization problem (SOOP). The search space is then randomly initialized with a set of initial points. Second, in order to supply locations on the Pareto boundary, the TR method solves the SOOP. Finally, all points on the Pareto frontier are obtained using CS. A range of MO benchmark problems have demonstrated the efficiency of the proposed algorithm (TR based CS) in generating Pareto optimum sets for MOOPs. Furthermore, a demonstration of the suggested algorithm's ability to resolve real‐world applications is provided through a practical implementation of the algorithm to improve an abrasive water‐jet machining process (AWJM). [ABSTRACT FROM AUTHOR]

Additive manufacturing, particularly laser powder bed fusion (L-PBF), is an emerging method for fabricating complex parts in various industries. However, it faces the persistent challenge of thermal deformation, a significant barrier to its wider application and reliability. Current strategies, while partially effective, do not fully address the intricate thermal dynamics of the process, indicating a clear research gap in optimizing manufacturing techniques for better thermal management. This study focuses on understanding and mitigating thermal deformation in L-PBF using Genetic Algorithms (GAs). The application of GAs as a 'black-box' approach is explored to gain insights into the complex physics of L-PBF. A comprehensive investigation into the optimization of island sequencing within L-PBF processes is presented, employing GAs to systematically reduce thermal deformation. Various island sequences in a bilayered block structure are analyzed to assess the effectiveness of GAs in minimizing deformation, including scenarios such as variations in block sizes and interlayer rotation angles. Statistical tools such as silhouette scores and probability density distribution plots are utilized to provide a thorough analysis of deformation patterns and their respective thermal behaviors. The results show GA's remarkable efficiency in enhancing thermal management, achieving a significant reduction in thermal deformation within a range of 12–15% across the examined scenarios. This achievement highlights GA's capability in rapid optimization of scan sequences for better thermal deformation control. The findings enhance the understanding of thermal dynamics in L-PBF and consequently open new avenues for improving the quality and reliability of other metal additive manufacturing processes as well. [ABSTRACT FROM AUTHOR]

In this article vibrations transmitted to the frame of a packaging machine are reduced through dynamic balancing. It is known that unbalanced inertial forces and moments result in undesirable vibrations that negatively affect the performance and durability of machines. These shaking forces and shaking moments must be completely balanced to eliminate the vibrations. Balancing generally increases other dynamic characteristics like driving torque, bearing reactions in joints and the overall mass of the mechanism and can lead to a more complex mechanism. Instead of a complete balance of shaking force and moment, these can be minimized through optimization techniques. In this work, an optimization procedure is formulated to determine the masses and locations of counterweights that provides the minimum shaking force and moment of a packaging machine sealing head, thus reducing the vibrations transferred to the frame. [ABSTRACT FROM AUTHOR]

We propose an automatic method for generating flight summaries of prescribed duration, given any planed 3D trajectory of a flying object. The challenge is to select relevant time‐ellipses, while keeping and adequately framing the most interesting parts of the trajectory, and enforcing cinematographic rules between the selected shots. Our solution optimizes the visual quality of the output video both in terms of camera view and film editing choices, thanks to a new optimization technique, designed to jointly optimize the selection of the interesting parts of a flight, and the camera animation parameters over time. To our best knowledge, this solution is the first one to address camera control, film editing, and trajectory summarizing at once. Ablation studies demonstrate the visual quality of the flights summaries we generate compared to alternative methods. [ABSTRACT FROM AUTHOR]

This paper presents a method for calculating the Region of Attraction (ROA) of nonlinear dynamical systems, both with and without control. The ROA is determined by solving a hierarchy of semidefinite programs (SDPs) defined on a splitting of the time and state space. Previous works demonstrated that this splitting could significantly enhance approximation accuracy, although the improvement was highly dependent on the ad‐hoc selection of split locations. In this work, we eliminate the need for this ad‐hoc selection by introducing an optimization‐based method that performs the splits through conic differentiation of the underlying semidefinite programming problem. We provide the differentiability conditions for the split ROA problem, prove the absence of a duality gap, and demonstrate the effectiveness of our method through numerical examples. [ABSTRACT FROM AUTHOR]

Pursuing electric mobility has led to a growing demand for efficient battery enclosures that can withstand dynamic forces and vibrations. This study focuses on advancing the structural integrity and vibrational resilience of battery enclosures through a holistic optimization approach. This research identifies optimal design parameters that minimize deformation and stress while maximizing resonance frequency by leveraging finite element analysis, modal analysis, and multi-objective optimization techniques. The study unveils three candidate designs that showcase remarkable improvements, including a 49.41% reduction in deformation, a 35.79% reduction in stress, and a 19.92% increase in resonance frequency. These findings underscore the potential of integrated design strategies to drive innovation in sustainable electric vehicle technologies. [ABSTRACT FROM AUTHOR]

We present a study of a selection of metal ornaments belonging to archaeological funerary goods from Patagonic Indigenous groups. They are part of the 'Andrés Giai' Collection, under the guard of the Museum of Patagonia (Nahuel Huapi National Park [PNNH]; National Parks Administration [APN]), Argentina. The absence of contextual information and its current conservation status motivated the design of an interdisciplinary methodology to develop an Integral Conservation Plan that considered the selection of analytical techniques. Here, we implemented complementary nondestructive characterisation techniques such as particle induced X‐ray emission (PIXE), X‐ray diffraction (XRD) and scanning electron microscopy coupled with energy dispersive X‐ray spectroscopy (SEM/EDS). We developed a methodology enabling us to characterise each piece with respect to its metallic composition and degradation products, and in certain cases, their manufacturing process. These findings have contributed with essential information for the tailored conservation treatment proposal. Moreover, they provided key information in regard to the reconstruction of contextual data for each piece. In addition, the methodology proposed for the Integral Conservation Plan takes into account an optimisation of the techniques to be used in order to minimise unnecessary handling of goods whilst ensuring an appropriate use of technological and economic resources whilst minimising time on potentially unconclusive measurements and results. [ABSTRACT FROM AUTHOR]

Aiming at the weak robustness of possibilistic fuzzy clustering against noise, a robust possibilistic fuzzy additive partition clustering with master–slave neighborhood information constraints is proposed for high noise image segmentation. This algorithm first constructs a master–slave neighborhood model, which consists of the master neighborhood window of the current pixel and the slave neighborhood window around the master neighborhood pixel. Then, the master–slave neighborhood information is integrated into the possibilistic fuzzy additive partition clustering model, and a novel robust possibilistic fuzzy clustering model incorporating deep local information is constructed. Next, this clustering model is further simplified by Cauchy inequality and a robust master–slave neighborhood information-driven possibilistic fuzzy clustering algorithm is derived by optimization theory. Extensive experimental results indicate that the proposed algorithm is very effective for noisy image segmentation, and its segmentation performance is significantly better than many existing state-of-the-art fuzzy clustering-related algorithms. In short, the work of this paper has profound significance for the development of robust fuzzy clustering theory. [ABSTRACT FROM AUTHOR]

This paper proposes a streamlined approach to addressing the problem of allocating finite crew resources to concurrent jobs in the context of municipal drainage infrastructure maintenance. The problem was defined from the perspective of a project manager involved in planning such operations on a day-by-day basis. The problem statement was then transformed into a simplified Integer Linear Programming optimization model. Performance metrics were devised to evaluate the optimization model's effectiveness. A heuristic algorithm representing the decision-making process by a seasoned planner in the partner company was also developed. Both methods were applied to a case study and contrasted based on the same performance metrics. The findings underscored substantial optimization benefits in rendering decision support in resource-constrained drainage construction operations planning. In conclusion, this research presents an alternative strategy for navigating the complexities inherent in finite crew resource allocation on multiple concurrent drainage projects; lends a cost-effective optimization solution to improving the utilization of finite available crews while satisfying service demands from multiple clients to the largest extent possible. [ABSTRACT FROM AUTHOR]

Scissor structures, widely used in kinetic architecture, have been researched for over six decades. However, most studies focus on the deployability of the scissor structures, often disregarding the potential transformability of scissor loops that allow changing the curvature. Three scissor units, namely, translational, polar, and angulated units, do not adhere to the trajectory of an arc suitable for a transformable canopy structure. Although the deployment route of the polar version aligns with this trajectory, it is impractical for achieving transformability due to deviations. Therefore, the paper presents a novel approach to optimize generalized scissor linkages (GSLs), consisting of symmetrical and identical scissor units. Defined by three variables, GSL is introduced to address the transformability issue, and the optimization method is developed. According to this method, the selected polar scissor is modified to synthesize the GSL version. It is demonstrated that no GSL is composed of identical and symmetrical units deploying precisely along the arc trajectory. Nevertheless, the proposed optimization method enables the output GSL to converge toward the desired route. Three basic scissor unit types can be represented by a single parameterized GSL, effectively unifying different scissor linkages under a single definition. Practical Applications: Scissor linkages find extensive applications in kinetic architecture, such as domes, canopies, tents, emergency bridges, temporary shelters, and responsive facades. Existing studies on such linkages predominantly focus on deployed and contracted configurations of these mechanical structures. This study diverges by attempting to establish intermediate phases for designing such structures that can adapt to changing environmental conditions or user needs. To achieve the objective, the scissor linkage must move within a defined range, conceptualized as an arc of a circle. For instance, by tracking the movement of the sun, one can ensure a consistent positioning of shadowed areas beneath an architectural canopy. None of the existing scissor linkages composed of basic translational, polar, and angulated units in the literature moves along this trajectory precisely. Only polar linkages approximate the desired route despite their large deviations. The generalized scissor linkage (GSL) introduced in the paper with distinct parameters demonstrates fewer deviations. Hence, an attempt is made to synthesize the GSL as the optimized version of the polar linkages. The mathematical foundation of the optimization method is introduced. It is foreseen that this method holds the potential to evolve into a versatile design tool in the future. [ABSTRACT FROM AUTHOR]

The sliding mode controller has been designed for joint position tracking and link vibration reduction of the two-link flexible manipulator. In this work, to track the desired trajectory, sliding mode controller has been developed with two different types of sliding surfaces, to show the effect of sliding surfaces on the system's performance. Sliding surface plays an important role in the stability and control of the system. The sliding surface also acts as a dynamic equilibrium surface where the system states converge to track the desired behavior with minimum error. In this work, the PID sliding surface and PD sliding surface have been considered. A comparative analysis has been presented. The proportional, integral, and derivative gains for each sliding surface have been tuned using optimization techniques. Arithmetic optimization algorithm and FOX optimization algorithm have been used here. In this work, mass of the payload has been considered an uncertain parameter. To demonstrate the efficiency of the suggested controllers, the value of the uncertain parameter has been altered. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%. [ABSTRACT FROM AUTHOR]

Let ${\mathfrak g}$ be a complex simple Lie algebra and ${\mathfrak n}$ the nilradical of a parabolic subalgebra of ${\mathfrak g}$. We consider some properties of the coadjoint representation of ${\mathfrak n}$ and related algebras of invariants. This includes (i) the problem of existence of generic stabilizers, (ii) a description of the Frobenius semiradical of ${\mathfrak n}$ and the Poisson center of the symmetric algebra , (iii) the structure of as -module, and (iv) the description of square integrable (= quasi-reductive) nilradicals. Our main technical tools are the Kostant cascade in the set of positive roots of ${\mathfrak g}$ and the notion of optimization of ${\mathfrak n}$. [ABSTRACT FROM AUTHOR]

The hyperplane theory plays a very important role in the research of optimization, it can help us better understand and solve various optimization problems. Therefore, the development of hyperplane theory has always been concerned by the scholars. This paper mainly studies the quasi-support hyperplane, which is the generalization of a support hyperplane, on the asymmetric normed space. First, the properties of the distance from a point to a non-empty set are studied more comprehensively, the isometric property of the distance from a point to a half space and to the corresponding hyperplane is proved. Second, the problem of supremum (infimum, resp.) of a linear function on a set is reduced to the problem of upper quasi-support (lower quasi-supports, resp.) hyperplane of the set. Additionally, some equivalent conditions for the quasi-support hyperplane are given by using the theory of dual space of asymmetric normed spaces. The obtained results enrich the functional analysis in asymmetric normed spaces and have great significance to applications in optimization theory, approximation problems, complexity analysis and the other fields. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology to build mathematical models based on evidence and data sets, considering data and model uncertainty. We study the evolution of obesity in the population, being obesity a consequence of the transmission of unhealthy lifestyle habits and behavioral patterns influenced by social networks (family, friends, peers, etc.).We propose a three-step methodology. First, we create a synthetic data set based on a previous model with real data. Then, we search for dynamic models based on difference equations that best fit the dynamics described by the dataset and their uncertainty (uncertainty-aware). To do this, we use a dynamic structured grammatical evolution algorithm (an algorithm that builds possible models) on which we have defined a grammar (set of possible expressions that can be part of the model). The definition of appropriate grammar is crucial because it allows us to build models that do not contradict the knowledge of the phenomenon studied. However, the data may suggest introducing new terms that indicate the influence of unknown factors. Finally, from among all the models obtained, we will algorithmically search for a selection of them that best describes the uncertainty of the data. This methodology can be applied to various scenarios with available datasets and a limited understanding of the phenomenon. It aims to generate models that not only achieve precision but also incorporate terms that correspond to identifiable processes, which can be explained within the context of the study problem. [ABSTRACT FROM AUTHOR]

Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization. [ABSTRACT FROM AUTHOR]

Effective decision-making in urban water infrastructure optimization, particularly in sustainable urban drainage systems (SuDS), hinges on navigating complex multi-objective problems. This study addresses the challenge of sparse Pareto-fronts in many-objective SuDS design, often caused by algorithmic limitations and the intricate objective function interactions, impacting the availability of diverse design alternatives for decision-makers. To tackle these challenges, this research proposes a novel framework that integrates advanced data imputation and surrogate modeling techniques. The framework uses artificial intelligence methods to populate the sparse regions by replicating the Pareto-front structure, predicting decision variables to guide further simulations and find efficient solutions without repeated optimization runs. The methodology is validated through a SuDS design case study located in Ann Arbor, Michigan. Following the initial optimization, sparse regions were identified in four of the eight objective functions. Using the proposed framework, 32 new and efficient SuDS designs were introduced into the sparse regions without additional optimization, enhancing the uniformity of the Pareto front. This study enhances decision support tools in urban flood management by increasing the informativeness of design alternatives available to planners and engineers. [ABSTRACT FROM AUTHOR]