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Researchers are diligently striving towards generating sustainable successors for man‐made fibers. Naturally derived fibers/fillers have gained much attention these days due to their use in the development of renewable and biodegradable components. This research emphasizes the physical, mechanical and tribological aspects of silanized Corchorus olitorius particle‐based thermoset composites. With the help of a contact lay‐up technique, varied weight fractions (0, 2.5, 5, 7.5, 10 and 12.5%)‐based composites as per ASTM standards were generated. The experimental results reveal that silanization promotes the interfacial relationship between particles of Corchorus olitorius and the matrix and also the established silanized particle‐based composites demonstrated a potential modest density feature. The minimal weight fraction‐based composite exhibited the best mechanical and wear‐resistant features. Furthermore, the interval‐valued intuitionistic fuzzy Schweizer–Sklar power weighted average approach has been developed as a distinctive structure for obtaining the most effective composite material for any further industrial applications. From this optimization strategy, the specimen with 5 wt% particle content is found to be the best, followed by the specimen with 7.5 wt% particle content. This work highlights the crucial function of surface modification techniques, especially silanization, in enhancing the effectiveness and stability of composites that include Corchorus olitorius particles. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Process heat electrification offers the prospect of deep decarbonization of the chemical and allied industries. Replacing fossil fuel‐fired boilers with electric units can reduce carbon emissions if the power mix has a large share of renewables. For multinational firms with plants in multiple locations, the electrification decisions should be scheduled based on grid carbon intensity projections and should also be coordinated among these subsidiaries. In addition, carbon credits can be traded among the multiple sites to allow lagging plants to reduce their carbon footprints. A novel mathematical model has been developed to optimize process heat electrification plans in multinational corporations. The model determines the optimal timing of electrification at each location, and also the necessary level of carbon trading among subsidiaries. An illustrative case study demonstrates how the model can be used to generate electrification plans that are superior to those based on simple heuristics. [ABSTRACT FROM AUTHOR]

Summary: Node localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization. [ABSTRACT FROM AUTHOR]

Stress‐constrained reliability‐based topology optimization (RBTO) method has incurred considerable attention owing to its superiority of enhancing the structural safety. However, the traditional reliability methods encounter inaccurate issue for evaluating the failure probability of stress‐constrained structure. In this work, the failure mechanism of the stress‐constrained RBTO problem is analyzed for continuum structure, which reveals that the correlation between different stress constraints and utilization of aggregation function significantly impacts the accuracy. Then, a novel stress‐constrained system RBTO framework is suggested to enhance computational efficiency and accuracy for system reliability analysis. Furthermore, an accurate and efficient semi‐analytical method is suggested to approximate the performance functions through first‐order Taylor series expansion, in which the intricate implicit expressions are substituted by the straightforward analytic expressions. In addition, the fidelity transformation method is employed for converting the semi‐analytical RBTO method to classical RBTO method. To demonstrate the practicability of the proposed framework, three benchmark cases, including 2D and 3D problems, are tested. The results reveal that the proposed framework achieves high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this study, we investigated the potential energy surface of BXY3 (X = B, Al, Ga; Y = C, Si, Ge) clusters employing a few global optimization techniques. Remarkably, the global minimum structure obtained for most of the cases revealed a planar tetracoordinate boron atom, shedding light on the inherent stability of this motif. A comparative analysis of the performance of the different global optimization techniques employed is presented, offering insights into their efficacy. Additionally, the overall stability of the obtained global minimum structures is thoroughly examined through Atom‐centered Density Matrix Propagation (ADMP) simulations spanning 20 ps at temperatures 300 and 500 K. The aromaticity of the respective clusters is also assessed via Nucleus Independent Chemical Shift (NICS) and Isochemical Shielding Surface (ICSS) calculations, providing valuable information regarding their electronic structure and stability. This comprehensive theoretical investigation contributes to our understanding of the structural properties of these clusters, with implications for their potential applications in various fields of chemistry. [ABSTRACT FROM AUTHOR]

This article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios. [ABSTRACT FROM AUTHOR]

Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (>20%) with a lower acoustic impedance of 6000–8500 m/s g cm3, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two‐way travel time, provides high‐resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high‐resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity. [ABSTRACT FROM AUTHOR]

Airborne wind energy (AWE) technology aspires to provide increased options for wind energy harvesting at a lower cost than conventional turbines. As AWE technology is still in its infancy, there is relatively little published information concerning its aerodynamic details. In this study, we apply the FLOWUnsteady suite of aerodynamic analysis tools—including a vortex particle method for wakes and actuator line methods for rotors and wings—to the analysis of a fixed‐wing, on‐board generation, wind harvesting aircraft (or windcraft for short). Specifically, we explore variations in the rotation directions and vertical and horizontal (front to back) spacing of the rotors to understand the complex interactional aerodynamics of a multirotor windcraft design. Our results indicate that for the configurations presented herein, the rotor‐on‐rotor and wing‐on‐rotor interactions induce maximum total variations on cumulative rotor performance of less than 3% relative to baseline, with most configurations varying from baseline by 2% or less. In addition, rotor‐on‐wing interactions cause decreases in wing lift coefficient of up to roughly 7% (for the configurations simulated) relative to the isolated wing. Furthermore, through selecting specific rotor rotation directions, it is possible to prescribe a portion of the shape of the wing lift distribution. Further exploration, perhaps with the assistance of optimization techniques, may yield greater insights into this uncharted space of windcraft interactional aerodynamics. [ABSTRACT FROM AUTHOR]

In this article, the response surface approach was employed to enhance the hydraulic performance of the pump at the rated point. Specifically, an approximate link between the design head and efficiency of the single‐stage centrifugal pump and the parameters of the impeller's design was established. The first step in creating a one‐factor experimental design involved selecting significant geometric variables as factors. Decision variables such as the number of blades, flow rate, and rotation were chosen due to their significant impact on hydraulic performance, while head and efficiency were considered as responses. Subsequently, the best‐optimized values for each level of the parameters were identified using response surface analysis and a central composite design. The impeller schemes of the Design‐Expert software were evaluated for head and efficiency using Computational fluid dynamics, and a total of 20 experiments were conducted. The simulated results were then validated with experimental data. Through the analysis of the individual parameters and the approximation model, the ideal parameter combination that increased head and efficiency by 7.90% and 2.06%, respectively, at the rated value was discovered. It is worth noting that in cases of a high rate of flow, the inner flow was also enhanced. [ABSTRACT FROM AUTHOR]

Numerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two‐step real‐time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self‐optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation. [ABSTRACT FROM AUTHOR]

ABSTRACT This study explores nitrogen fixation in the Martian atmosphere using plasma technology. By combining microwave and dielectric barrier discharges (DBD), nitrogen was converted into water‐soluble nitrogen fertilizer (N₂O₅) for agriculture. Despite the low pressure and temperature of Mars, optimized discharge conditions and reactor design significantly boosted the generation efficiency of N₂O₅ precursors, NO₂, and O₃ production. The microwave discharge achieved NOx concentrations of 63.6 ppm, close to experimental values; adjusting DBD pressure to 760 Torr enhanced O₃ concentration by 105 times, yielding 3.8 mol/m³ N₂O₅. The findings lay a technological foundation for future Martian ecosystem development and highlight the need for further experimental validation and system optimization. [ABSTRACT FROM AUTHOR]

Port collecting and distributing network (PCDN) carries both freight traffic flow associated with the port and urban traffic flow, serving as the only channel between the port and the hinterland. The congestion caused by increasing freight traffic seriously disrupts urban traffic and leads to environmental issues such as increased carbon emissions. To address this issue, this study proposes a multiobjective optimization approach for the route selection of freight traffic flow within the PCDN, considering the balance among efficiency, environmental performance, and disruption to urban traffic. First, the generation mechanism and characteristics of freight traffic flow in the PCDN are analyzed, followed by the development of a mathematical model based on the static traffic flow distribution theory and the multiobjective optimization theory. Then, a solution framework with NSGA‐III as the core is developed, and an improved Dial algorithm is utilized to allocate traffic flow. Finally, taking a large‐scale container port as the case study, the solution framework is implemented to address the multiobjective optimization model and obtain the optimal route for freight traffic. The results show that there are significant negative correlations between distributing efficiency and disruption to urban traffic, as well as between distributing efficiency and carbon emissions. Decision‐makers can choose the optimal route according to different preferences or adopt the compromise solution by referencing the Pareto front obtained by the solution framework. The proposed method provides theoretical support for designing the PCDN scientifically. [ABSTRACT FROM AUTHOR]

In this work, an attempt is made to find out the optimized weld parameters in double‐pulse metal inert gas‐welded 15CDV6 high‐strength low‐alloy steel. Three parameters namely wire feed speed, pulse frequency, and amplitude were varied and the weld quality was assessed based on bead width, depth of penetration, and tensile strength. Best and worst weld parameter combinations were identified using Technique for Order of Preference by similarity to ideal solution optimisation technique. Weld obtained through optimised parameter combination had a tensile strength of 867.28 MPa and the weld obtained through worst weld parameter combination had a tensile strength of 212.21 MPa. Compared to the base metal, the best weld had 21% higher tensile strength, and the worst weld had 65% lower tensile strength. A higher percentage of martensite in the microstructure of the best weld played a major role in achieving a high value of tensile strength. The best weld also had lower percentage of ferrite in both the heat‐affected zone and the weld zone compared to the worst weld. Best weld also had a lower percentage of high strain regions than the worst weld as indicated by electron backscatter diffraction results. [ABSTRACT FROM AUTHOR]

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate‐scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit‐model‐based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non‐deterministic nature of photonic entangling gates. Here, a variational learning approach is presented for designing quantum photonic circuits, which directly incorporates post‐selection and elementary photonic components into the training process. The complicated circuit is treated as a single nonlinear logical operator and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control achieved by genetic algorithm, the internal parameters of the chip are adjusted and optimized in real‐time for task‐specific cost functions. A simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate is utilized to illustrate how the proposed approach works, and then the approach is applied to the first demonstration of quantum stochastic simulation using integrated photonics. [ABSTRACT FROM AUTHOR]

This paper develops a mathematical optimization approach for link‐level air traffic flow management. First, a pure integer programming model is established for the problem on a link level. The model seeks to minimize the weighted summation of ground delay and airborne delay, and offers a more precise modeling for the capacity and traffic dynamics at the merge, diverge, and intersection nodes. The optimization problem is computationally NP‐hard, and a modified genetic algorithm is then presented to solve it. Besides a new chromosome coding rule for converting binary decision into time series vectors, the algorithm uses a taboo mutation operator and an adaptive mutation probability selection operator. Finally, the effectiveness and superiority of the model and algorithm are demonstrated through a real‐world case study in Dalian, China, and some managerial insights are presented. [ABSTRACT FROM AUTHOR]

ABSTRACT Distributed robust optimization algorithms focus on developing decision‐making strategies that can operate effectively under uncertain conditions. This paper examines a scenario‐based distributed robust optimization algorithm for optimal scheduling of virtual power plants (VPPs). The proposed algorithm follows three key steps: scenario sampling, scenario reduction, and distributed optimization using the Alternating Direction Multiplier Method (ADMM). This approach balances robustness with computational complexity and ensures convergence, offering a practical solution for multi‐agent optimization. By employing an uncertainty set to represent the variabilities of wind and photovoltaic power generation, which leads to the establishment of a distributed robust optimization model for optimal virtual power plant scheduling. Experimental simulations validate the algorithm's feasibility and efficacy in economically optimal scheduling, offering methodological support for enhancing both robustness and economic efficiency in VPPs' operations. [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]

Solar photovoltaics, especially rooftop systems also called distributed solar photovoltaics, are crucial in the ongoing energy transition. Modeling these systems is vital to understanding their role in a decentralized energy system. While ground‐mounted photovoltaic power plants are easier to model, generalizing yield profiles for rooftop systems is challenging. This study aims to estimate yield loss effects for rooftop solar photovoltaic systems compared to optimized ground‐mounted systems. Anticipated yield losses are 18% for residential, 7% for commercial, and 4% for industrial rooftop systems. The impact on residential prosumers' viability is assessed by comparing prosumer system optimization results with and without yield losses. Results show a non‐uniform change in installed solar photovoltaic and battery capacities, with a tendency to compensate for reduced yields by increasing photovoltaic capacity by up to 20%, given favorable cost prospects by 2050. The annualized total cost of energy for prosumer households could therefore increase by up to 20% by 2050. Despite yield reductions, installing a solar photovoltaic prosumer system remains more favorable than relying entirely on‐grid electricity. This study highlights the importance of considering yield losses in rooftop solar photovoltaics and the significant role of prosumers despite identified yield losses. [ABSTRACT FROM AUTHOR]

Although the use of optimization techniques for environmental and economic dispatch of integrated electricity and natural gas systems has been widely applied, there are still significant challenges in meeting the multiple energy demands of conventional and renewable energy sources, mainly wind‐solar. In this study, Wasserstein distance is introduced to measure the randomness of wind‐solar power generation to construct the uncertainty set. A multi‐objective distributionally robust optimization (MODRO) environmental‐economic scheduling model for risk aversion that minimizes the risk cost, the system operation cost, and the carbon emission cost is proposed to achieve the balance between the risk cost, the operation cost, and the pollutant emission. To solve the model efficiently, the multi‐objective whale optimization algorithm (IMOWOA) was improved and used the 4‐node power system and the 7‐node natural gas system as case studies. The results show that the MODRO environmental‐economic scheduling model can measure the operational risk due to the stochastic fluctuation of wind‐solar energy sources, and provide an effective decision‐making tool for policymakers. Considering P2G technology and gas turbines at the same time, it promotes the coupled operation of electric‐gas integrated systems and achieves good economic efficiency. Thus, the model provides an effective solution for the stability, economy, and cleanliness of the integrated electric gas system. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Due to the complex soil environment and continuous traction ground return, the electrified railway grounding grids (ERGG) are easily susceptible to corrosion, which seriously threatens the safe operation of the traction power supply system (TPSS). In this paper, the corrosion evaluation model is presented based on electrical network theory. In order to solve the equations with multi‐variable and high‐dimension characteristics of the model, a two‐stage optimization (TSO) algorithm is proposed. In the first stage, the CPLEX solver (CPLEX optimizer produced by IBM) is adopted to obtain the resistance value of multiple branches, which provides the initial solver and search scope for the next step. In the second stage, the particle swarm optimization (PSO) algorithm is used to determine the optimal solution so as to minimize the node potential difference. Finally, a corrosion experimental system is designed and established to verify the effectiveness of the above algorithm. The results show that TSO algorithm can accurately diagnose the corrosion state of grounding conductors and adapt to the various scenarios of ERGG. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper addressed the trade‐off between timeliness and reliability in joint communication and over‐the‐air computation offloading (JCACO) system under short‐packet communications (SPCs). The inevitable decoding errors introduced by SPC lead to errors in the data aggregation process of over‐the‐air computation (AirComp). Due to limited resources, pursuing high reliability may prevent the JCACO system from meeting delay requirements, resulting in a trade‐off between reliability and timeliness. To address this issue, this paper investigates the timeliness and reliability of the JCACO system. Specifically, the moment generating function method is used to derive the delay outage probability (DOP) of the JCACO system, and the outage probability of AirComp is calculated based on the errors that occur during its data aggregation process. The paper established an asymptotic relationship between blocklength, DOP, and AirComp outage probability (AOP). To balance timeliness and reliability, an AOP minimization problem is formulated under constraints of delay, queue stability, and limited resources based on computation offloading strategies and beamformer design. To overcome the issues of slow convergence and susceptibility to local optima in traditional algorithms, this paper proposed a stochastic successive mean‐field game (SS‐MFG) algorithm. This algorithm utilizes stochastic continuous convex approximation methods to leverage Nash equilibria among different users, achieving faster convergence to the global optimal solution. Numerical results indicate that SS‐MFG reduces AOP by up to 60%, offering up to a 20% improvement in optimization performance compared to other algorithms while also converging faster. [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]

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]

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]

Recently, Migórski‐Dudek investigated a steady Oseen flow for a generalized Newtonian incompressible fluid with unilateral and frictional‐type boundary conditions. They established an existence theorem for the steady Oseen model when the divergence‐free convection field b$\mathbf {b}$ and acting volume force f$\mathbf {f}$ are known. However, prior knowledge of b$\mathbf {b}$ and f$\mathbf {f}$ is often impossible in practical engineering applications. This leads to the question of whether the divergence‐free convection field b$\mathbf {b}$ and acting volume force f$\mathbf {f}$ can be determined. The main objective of this paper is to provide a positive response to this challenging and intriguing question. Specifically, we aim to formulate an inverse problem for identifying b$\mathbf {b}$ and f$\mathbf {f}$, characterized by a nonlinear and nonsmooth regularized optimization problem. Initially, we introduce a variational selection mapping for the steady Oseen model. We then establish the boundedness and generalized continuity of this variational selection. Finally, by leveraging optimization theory, convex analysis, and nonsmooth analysis, we develop an abstract regularization framework for the considered inverse problem and establish the existence of a solution. [ABSTRACT FROM AUTHOR]

Summary: In this article, an adaptive deep neural network (DNN) optimized control strategy is developed for a class of nonlinear strict‐feedback systems with prescribed performance. First, the DNN is applied to approximate the unknown function, and the weight update law is designed to reduce the mathematical challenge based on the first‐order Taylor's series. Second, the optimized backstepping technique is utilized to construct virtual and actual controllers in the backstepping process to achieve the overall control optimization of the system. Next, a control strategy based on the time‐varying switching function and the quartic barrier Lyapunov function is employed to achieve the prescribed performance. Then, the tracking error can converge to the prescribed accuracy within the prescribed time, and every signal within the system has a bound. Finally, the particle swarm optimization algorithm is utilized to search for the designed parameters and simulation examples to verify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

As aircraft electrification advances, permanent magnet synchronous motors (PMSMs) require higher power density and efficiency, but optimisation is hindered by high computational costs and resource consumption. To address this, the paper proposes a multi‐objective optimisation method based on adaptive sampling radial basis function (ASRBF). The ASRBF algorithm adaptively adds sample points by estimating expected improvements at prediction points, enabling the surrogate model to rapidly approximate the global optimum while significantly reducing function evaluations. It integrates optimisation objectives and constraints using probabilistic improvement techniques, enhancing robustness and convergence speed by avoiding excessive exploration of invalid regions. Mathematical test functions validate ASRBF's excellent performance in handling complex objective domains. Applied to high‐speed PMSM with hollow conductors, it aims to minimise AC losses while maximising slot fill factor and heat dissipation, resulting in a 15% reduction in losses and an increase in conductor heat dissipation area and slot fill factor, at one‐thousandth of the cost of the full factorial optimisation method. The ASRBF algorithm efficiently constructs surrogate models for multi‐dimensional, multi‐objective, non‐linear, and constrained problems, providing a powerful tool for comprehensive performance optimisation of complex systems such as motors. [ABSTRACT FROM AUTHOR]

This paper investigates the finite‐time H∞${{H}_\infty }$ fault detection problem for large‐scale power systems via the Markov jumping mechanism subject to unknown disturbances. The novel power system is described by a large‐scale system model, and the residual dynamic properties of unknown input signals and fault signals, including unknown disturbances and modelling errors, are obtained by reconstructing the system. Then, the energy norm indicators of the residual disturbance signal and fault signal are, respectively, selected to reflect their suppression effect on disturbance and sensitivity to faults. Moreover, the design of a fault detection observer is formulated as an optimisation problem. Based on Lyapunov theory and linear matrix inequalities (LMI), sufficient conditions for the designed fault detection observer solutions are given, and an optimisation design method is provided. Finally, the simulation results show that the optimised observer can detect the fault signal effectively and can contain the effect of unknown disturbances on the residuals within a given range when a fault occurs. [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]

Extraordinary properties emerge from subsystems' interactions. Hybrid energy systems (HESs) are a promising concept that could change the renewable energy landscape. By co‐designing generation, storage, and conversion technologies, HESs can provide new electrical power services, increase grid stability and control authority, and generate energy and/or nonenergy products such as electricity, hydrogen, ammonia, heat, digital data, or fresh water. This article discusses some conditions the co‐design of HESs should follow to optimize the combined system (synergy), avoiding deterioration (dysfunction). It introduces some technoeconomic synergy conditions, develops a synergy margin, and analyses several case studies, exploring also the control co‐design methodology to optimize synergistically the hybrid system. [ABSTRACT FROM AUTHOR]

Aims: Suboptimal device programming is frequent in non‐responders to cardiac resynchronization therapy (CRT). However, the role of device optimization and the most appropriate technique are still unknown. The aim of our study was to analyse the effect of different CRT optimization techniques within a network meta‐analysis. Methods: A systematic search was conducted on MEDLINE, Embase and CENTRAL for studies comparing outcomes with empirical device settings or optimization using echocardiography, static algorithms or dynamic algorithms. Studies investigating the effect of optimization in non‐responders were also analysed. Results: A total of 17 studies with 4346 patients were included in the quantitative analysis. Of the treatments and outcomes examined, a significant difference was found only between dynamic algorithms and echocardiography, with the former leading to a higher echocardiographic response rate [odds ratio (OR): 2.02, 95% confidence interval (CI) 1.21–3.35], lower heart failure hospitalization rate (OR: 0.75, 95% CI 0.57–0.99) and greater improvement in 6‐minute walk test [mean difference (MD): 45.52 m, 95% credible interval (CrI) 3.91–82.44 m]. We found no significant difference between empirical settings, static algorithms and dynamic algorithms. Seven studies with 228 patients reported response rates after optimization in non‐responders. Altogether, 34.3%–66.7% of initial non‐responders showed improvement after optimization, depending on response criteria. Conclusions: At the time of CRT implantation, dynamic algorithms may serve as a resource‐friendly alternative to echocardiographic optimization, with similar or better mid‐term outcomes. However, their superiority over empirical device settings needs to be investigated in further trials. For non‐responders, CRT optimization should be considered, as the majority of patients experience improvement. [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]

This work suggests improved conjugate gradient methods for enhancing the efficiency and robustness of the classical conjugate gradient methods. The study modifies the diagonal of the inverse Hessian approximation of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton update in order to build a preconditioner for nonlinear conjugate gradient (NCG) methods applied to large‐scale unconstrained optimization problems. Damping techniques were embedded into the algorithm to impose the positive definiteness of the diagonal approximation. This made the methods easy to use and presented a viable way to increase the effectiveness of unconstrained optimization techniques. Experimental findings from a collection of benchmark problems demonstrate the efficiency and robustness of the proposed method when compared to five other existing NCG algorithms. Moreover, the successful application of the algorithm to manipulate robotic planar motion control systems with 3 degrees of freedom has been demonstrated, highlighting the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

Mixed potential theory is frequently employed for analysing large‐disturbance stability. The precision of the current stability criterion for DC microgrids controlled by virtual DC motors (VDCM), which relies on mixed potential theory, is inadequate. This is primarily because the criterion does not account for the control parameters of the DC bus voltage control link and the virtual DC motor link. To address these issues, the paper initially formulates the control aspect of the VDCM using a controlled current source. Subsequently, it derives the system's mixed potential function from the model, enabling the development of a stability criterion that includes the control parameters of the DC bus voltage control link, the virtual DC motor link, and the current tracking link. It also clarifies how parameters not directly included in the criterion affect system stability. Through simulations and experimental validations, it is demonstrated that the proposed stability criterion effectively captures the impact of control parameters on system stability and precisely delineates the system's power stability boundary, offering insights for system parameter optimization. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources and intelligent energy management technologies, park‐level integrated energy systems (PIESs) are essential for multi‐energy flow conversion and utilization. However, coordinating the upstream energy sources, internal energy stations, distribution networks, and downstream loads within the PIES framework remains a challenge. Developing a station‐network coordinated planning scheme for PIES confronts challenges, such as overly complex models that are difficult to solve and the difficulty of implementing standard methods for medium and long‐term planning. This paper proposes a multi‐stage coordinated planning approach for PIES, containing energy stations, multi‐energy networks, and load aggregation nodes. The energy equipment and energy networks are precisely modelled to enhance the reliability of the planning scheme. The chance‐constrained programming (CCP) method is adopted to address uncertainties arising from renewable energy generation. Additionally, the improved big‐M method and second‐order cone (SOC) relaxation technique are utilized to manage non‐linear elements, transforming the model into a mixed‐integer second‐order cone programming (MISOCP) form to enhance solution efficiency. A case study illustrates that the proposed method enables effective allocation of equipment and networks, outperforming single‐stage planning by reducing investment costs, enhancing facility utilization rates, and facilitating renewable energy integration. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Networks (MANETs) are crucial wireless networks for military, corporate, and emergency use, yet they are vulnerable to disruptions from malicious nodes. The presence of malicious nodes can lead to message transmission and routing disorganisation, and network performance is effectively compromised. Game theory‐based fuzzy secure clustering (GTFSC) improves performance metrics in low‐scale and high‐scale networks. This protocol's novel ability to dynamically scale performance measures as nodes expand improves efficiency and adaptability. While improving performance metrics, the proposed algorithm also efficiently identifies malicious nodes and re‐routes the transmission, excluding the found malicious nodes. For any MANET system, secure and successful data transmission is paramount. The proposed protocol integrates various algorithms to fulfil its aim of customised EGT, GWO, and fuzzy clustering. Black hole attacks, grey hole attacks, Sybil attacks, and data tampering attacks are all considered to provide comprehensive attacks on MANET. Every node is assigned trust values, which get updated on data transmission. Fuzzy Clustering is employed to identify malicious nodes. Evolutionary Game Theory (EGT) optimises network organisation by designating cluster heads and clusters as nodes. Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. The analysis result shows maximum performance with a packet delivery ratio of around 98%, throughput of 90% end‐to‐end delay reduced by 15%, and energy consumption reduced by 18%, respectively, compared to an existing protocol. [ABSTRACT FROM AUTHOR]

Smaller grazers consistently show greater preference for recently burned patches than larger species. Energy optimization theory posits that this pattern is driven by small‐ versus large‐bodied herbivores seeking to maximize energy intake by choosing high‐quality recently burned grasses, or high‐quantity unburned grasses, respectively. We propose that if burn preference is driven by an energy‐maximization mechanism, then preference should change over time as grass regrows and progresses across the optimal feeding heights of herbivores of increasing body size. To test this, we used a camera trap array in the Serengeti National Park to quantify changes in the relative preference for burned patches of seven ruminant herbivore species. We compared observed patterns to simulation results from a grass production‐herbivore patch selection model. Burn preference and herbivore body size scaled negatively for 6 months after fire, but this relationship disappeared after 7 months when smaller species stopped selecting burns, and larger herbivores selected burns after 10 months, in a reversal of classic grazer succession. Simulations recreated the former but not the latter relationship, suggesting that an energy‐maximization mechanism can drive allometric scaling of burn preference immediately after fire, but over longer periods, grazer‐driven feedbacks are required to explain large herbivore burn preferences. [ABSTRACT FROM AUTHOR]

This paper investigates the robust stabilization problem of linear delay systems with parametric uncertainty. First, by means of the argument principle, a delay‐dependent sufficient condition is derived for designing a feedback controller, with which the closed‐loop system can achieve robust stability. Then, based on the fundamental matrix of the linear delay systems, a constrained optimization problem is formulated for solving the feedback gain matrices. The effectiveness of the proposed method is verified by some simulation results. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a nonsingular fast terminal sliding mode control for the PAR 4 parallel robot, focusing on workspace optimization and trajectory planning. First, an analytical inverse kinematic model is derived to calculate the workspace, which is then extended using parameter optimization techniques. Next, a trajectory planning method based on the S‐curve approach is proposed to generate the desired path, with special attention to reducing the cycle time of pick‐and‐place operations using sequential quadratic programming method. Finally, a nonsingular fast terminal sliding mode control strategy is suggested to enable the robot to accurately track the generated trajectory. The stability of the closed‐loop system is analyzed using Lyapunov theory, and the effectiveness of the controller is validated through simulation results. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper delves into the prescribed‐time distributed optimization problem for multi‐agent systems (MASs), considering set constraints. First, we transform the challenging optimization problem from the prescribed‐time t$$ t $$‐domain into the asymptotic time τ$$ \tau $$‐domain through a time‐domain transformation (TDT) technique. This strategic shift substantially mitigates the analytical complexities inherent in the original formulation. Second, we develop a novel class of TDT‐based optimization algorithms, utilizing gradient projection and Lagrange multipliers. This algorithm is tailored to efficiently tackle the distributed optimization problem while adhering to the set constraints. To further alleviate the computational and communication overheads, we introduce a corresponding TDT‐based event‐triggered optimization algorithm. Moreover, a rigorous analysis of both the optimal solution to the optimization problem and the system's stability is given by using optimization theory and Lyapunov stability theory. This comprehensive analysis provides valuable insights into the behavior and performance of our proposed approach. Finally, two numerical simulation results are presented to demonstrate the feasibility as well as effectiveness of the offered distributed control arrangement. [ABSTRACT FROM AUTHOR]

Recently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues. [ABSTRACT FROM AUTHOR]

In this research, Net‐Zero Energy Houses (ZEHs) is investigated that harness regionally produced electricity from photovoltaic (PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This article examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. Two potential scenarios are proposed: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, the Monte Carlo sample average approximation (SAA) is applied to obtain solvable deterministic convex problems. Last, to demonstrate the practical applicability of these models, the proposed scenarios are implemented in the Jono neighborhood in Kitakyushu, Japan. [ABSTRACT FROM AUTHOR]

In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self‐designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA‐based systems. [ABSTRACT FROM AUTHOR]

To meet the strict requirement on whole chain of vaccine production, storage, transportation, and distribution, most researches have been done on cascade refrigeration systems to achieve high operation performance, while a research gap on the performance exploration of eco‐friendly refrigeration systems still exists. In this paper, a comprehensive thermodynamic model was established to analyze the operation performance of a pre‐cooled cascade refrigeration system with eco‐friendly refrigerants propane (R290) and ethane (R170). Based on the present thermodynamic model, the performance optimization on the R290‐R170 cascade refrigerator was made with considerations of degree of subcooling, degree of superheat, evaporation temperature, condensation temperature, cascade condensation temperature, and cascade temperature difference. Variations of the coefficient of performance, exergy destruction, and total exergy efficiency of the refrigeration cycle were analyzed. Two mathematical correlations yielding the optimal cascade condensation temperature and maximized coefficient of performance were developed by multilinear regression analysis. When the evaporation temperature is − 60°C, the maximized coefficient of performance and total exergy efficiency are 1.276 and 49.51%. This paper demonstrates the potential for improving the R290‐R170 cascade refrigeration system and furnishes the basis for further exploration on ultra‐low‐temperature refrigerators. [ABSTRACT FROM AUTHOR]

The inconsistency of conformal array elements poses a huge challenge to pattern synthesis. Based on convex optimization theory, this paper proposes a novel directivity pattern synthesis method. It can maximize the directivity of dual‐polarized conformal arrays with a low dynamic range ratio (DRR). Firstly, the directivity function is presented under a dual‐polarized conformal array. Then the pattern synthesis based on maximizing directivity is summarized as a multiconstraint optimization problem. In addition, the DRR constraint is introduced to control the weight range. By using Taylor expansion, the problem is transformed into a convex optimization problem and solved iteratively. Finally, simulation results demonstrate that the proposed method can effectively achieve a good directivity pattern with low sidelobe level (SLL) and low DRR. [ABSTRACT FROM AUTHOR]

The reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy. [ABSTRACT FROM AUTHOR]

The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability. This paper introduces an advanced online optimization technique for decomposing and identifying power quality disturbances (PQDs). Leveraging an improved variational mode decomposition method and an optimized support vector machine algorithm, the framework enables the detection of multiple PQDs even with minimal training data. [ABSTRACT FROM AUTHOR]