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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

In order to study the cutting performance of the straight edge hob and reduce the cutting power consumption model of the straight edge hob, this paper takes the cutting power of the straight edge hob as the minimum goal, and establishes the mathematical model of the optimization design of the straight edge hob based on the composite optimization method. The mathematical model is solved by MATLAB software. At the same time,the mowing characteristics of a roller blade were studied by investigating the relationship between the hob and the coordination of variables such as rotational speed and roller diameter with the mowing parameters. The parameter analysis of straight edge hob before and after structural parameter optimization is generated, and a design method is proposed based on this. After defining the objective function and constraint conditions, the influence of structural parameters on the power consumption and efficiency of the hob was determined by optimising the complex method; this could significantly adjust the hob parameters to lower its power consumption. The energy consumption of the optimized design is reduced by 11.1 % compared with the original scheme, and the optimization effect is remarkable. The results show that the best working parameters of the hob are cutting speed of 1000 r/min, sliding cutting angle and grinding edge angle of 25~30°. Moreover, practical tests demonstrated the feasibility of using the proposed method to design the straight edge hob to improve mowing performance and hob stability. This study can provide parameter foundation and an optimization method for lowering chopping power consumption of the roller mower blade. [ABSTRACT FROM AUTHOR]

Because of the overly averaged element sensitivity in the topological optimization of an irregular structure with a grid independent filter, a topological optimization model was built for the structural domain. The maximization of stiffness was first taken as the goal for the topological optimization of irregular structure stiffness. Subsequently, an element size filter was proposed to address the overly averaged local element sensitivity with the grid independent filter when the designed domain element size varied dramatically. Finally, the element sensitivity of the objective function was derived under the given constraints. A case study was then conducted on a naval gun mount with the maximization of structural flexibility as the objective function and the volume of structural material as a constraint. A stiffness optimization model based on the bi-directional evolutionary structural optimization algorithm was adopted for the topological optimization of the gun mount. Structural optimization was conducted for the gun mount with different shooting angles to realize its optimal stiffness and strength under the constraint of consistent material volume. The optimization results proved that the element independent filter proposed in this paper could be effectively applied in the topological optimization of an irregular structure and used to explore the topological optimization of the supporting structure under impact. [ABSTRACT FROM AUTHOR]

This paper presents an overview of pulp and paper mills (PPM) production processes, the resulting release of wastewater effluent loaded with wide range of pollutants and associated environmental impacts. The review highlighted the different types of functional materials and their modified forms employed as coagulants for pulp and paper mills industries effluent (PPME) treatment that have been intensively studied as a promising strategy for PPM to achieve cleaner and sustainable treatments in accordance with sustainable development goals (SDGs) "6-Clean water and sanitation", "9-Industry, innovation, and infrastructure", and "12-Responsible consumption and production". Standalone coagulation treatment processes are inherently ineffective towards meeting the increasingly stringent discharge requirements, coupled with their higher energy demand, and increased operational and maintenance costs. Owing to the recalcitrant nature of PPME contaminants, this review explored the effectiveness of the coagulation processes for decontamination of PPME. Furthermore, the review provides a state-of-the-art coagulation-based hybrid systems employed for enhanced PPME treatment. The process limitations, influencing factors and optimization techniques are highlighted. The review also highlights how sustained research in the subject area impacts on achieving cleaner production. The review also discusses coagulant classifications and the synergistic, antagonistic and shock load toxic effects of hybrid coagulants on toxicant biodegradation and their associated system efficiency. Moreover, it offers a guide for the development and application of sustainable hybrid-based coagulants for PPME treatment. The findings presented herein provide a vital theoretical foundation for sustainable solutions to improve coagulation-based hybrid systems efficiency and their scale-up towards potential commercialization. [Display omitted] • Pulp and paper mills produce effluents containing diverse and emerging contaminants. • Different materials and their modified forms are used as coagulants for PPME treatment. • The synergistic and shock load toxic effects of hybrid-based coagulants could be identified. • Hybrid systems are highly efficient techniques for PPME treatment. • The review highlights process weaknesses, influencing factors and optimization techniques. [ABSTRACT FROM AUTHOR]

The pump-jet propulsion is a low-noise propeller device for underwater navigation consisting of a duct, a rotor, and a front stator. Its high design speed, which predisposes the rotor to cavitation and negatively impacts its noise performance, and the presence of numerous parameters for each component render the design complex. This paper aims to develop an automated multi-performance optimisation system for pump-jet propulsions. The system integrates curve fitting of characteristic parameters and full-parameter modelling to achieve an automated workflow between the design, simulation, and optimisation processes. The parameters within the fully parameterised pump-jet propulsion system, which influence its hydrodynamic performance, cavitation performance, and noise performance, are selected as optimisation design variables. Performance in terms of cavitation noise and hydrodynamics is chosen as the optimisation goal function. We set the torque and unsteady force of the propeller as constraints, thereby establishing a multi-objective optimisation design methodology for pump-jet propulsions. Compared to traditional optimisation methods, which typically modify only a few geometric variables, the approach presented in this paper rapidly identifies potential optimal solution regions within a complex, fully parameterised design space. It offers comprehensive evaluations of cavitation noise and hydrodynamic performance from multiple perspectives, effectively addressing the trade-off between noise reduction and efficiency often encountered in conventional designs. [ABSTRACT FROM AUTHOR]

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference. [ABSTRACT FROM AUTHOR]

To assess a subject with multiple factors or attributes, a comprehensive evaluation index, or say a composite indicator, is often constructed to make a holistic judgement. The key problem is to assign weights to the factors. There are various weighting methods in the literature, but a gold standard is lacking. Some weighting methods may lead to a trivial weight assignment that is one factor having a weight equal to 1 and the others all zero, while some methods generate a solution contradicting intuitive judgement, or even infeasible to calculate. This paper proposes a new model to generate weights based on the maximum entropy-minimum residual (MEMR) principle, directly estimating the relationship between factor weights and the composite indicator. The MEMR composite indicator extracts the common feature of multiple factors while preserving their diversity. This paper compares the MEMR model with other commonly used weighting methods in various case studies. The MEMR model has more robust, consistent, and interpretable results than others and is suitable for all comprehensive evaluation cases involving quantitative factors. The optimization technique of the proposed MEMR model and the related statistical tests are included as a package in the DPS (data processing system) software V21.05 for the convenience of application in all fields. [ABSTRACT FROM AUTHOR]

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose an optimized type-1 Sugeno fuzzy logic backstepping sliding mode controller (T1-SFL-BSMC) to control the altitude and rotational behaviour of a quadcopter. The proposed controller improves the performance of the system by overcoming the limitations of conventional trial-and-error techniques for BSMC gain design. Design/methodology/approach: In this study, the type-1 Sugeno fuzzy logic controller (T1-SFLC) technique is proposed which dynamically modifies the BSMC gains. In addition, the parameters of T1-SFLC are further tuned by five different optimization techniques, namely, genetic algorithm, particle swarm optimization (PSO), pattern search (PS), simulated annealing (SA) and adaptive neural network (ANN). Findings: The effectiveness of these optimization techniques in tuning the T1-SFLCs for the gain scheduled BSMC is evaluated in terms of time response metrics; rising time, settling time and root mean square error (RMSE) for altitude and rotational behaviour. The results demonstrated that PSO-optimized T1-SFL-BSMC provides better responses in terms of time response metrices with reduced rise and settling times and minimized RMSE when compared to other optimized T1-SFL-BSMC. Originality/value: This paper proposes a novel approach for controlling quadcopters by combining fuzzy logic with a BSMC. It is then improved by using many optimization strategies. A more dependable and effective approach to controller design is provided by the suggested method, which is essential for unmanned aerial vehicle applications. [ABSTRACT FROM AUTHOR]

In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and then these features are used as the input of support vector regression (SVR) for regression prediction. The Harris Hawk optimization (HHO) is used to optimize the hyperparameters of SVR, and the HHO-CNN-SVR algorithm is proposed. In order to verify the effectiveness of the algorithm for the prediction of the interception point and interception time, this paper constructs a dataset to test the method of simulating the missile interception maneuvering target. Compared with BP, ELM, SVR, HHO-SVR, and CNN-SVR models, the HHO-CNN-SVR model has outstanding performance in prediction accuracy and stability, especially for the interception time. The error is the smallest, and the error fluctuation is small. The MAE of the prediction result is only 0.0139 s; in the interception point prediction, the error of the range and elevation direction is significantly lower than that of the models used for comparison. The MAE in the range direction is 2.3 m, and the MAE in the elevation direction is 2.01 m, which meet the engineering requirements. The HHO-CNN-SVR model has strong prediction accuracy and stability in interception time and interception point prediction. In addition, different control strategies are used to construct a new prediction set, and noise is added to the prediction set. The HHO-CNN-SVR algorithm can maintain good prediction results. The results show that the HHO-CNN-SVR model proposed in this paper has strong generalization ability and high robustness, which can provide reliable support for penetration decision making and defense system optimization. [ABSTRACT FROM AUTHOR]

Purpose: Providing a comprehensive literature review to consolidate existing knowledge, advancements and future directions in the field. By synthesizing the state of research, this work enhances the understanding of Prescriptive Maintenance (PsM) methodologies, applications and potential benefits to assist researchers in identifying fruitful avenues for further investigation, and guide practitioners in implementing PsM strategies to improve maintenance outcomes in their industries. Design/methodology/approach: Through a systematic, multistage, specialists audited analysis of peer-reviewed articles, conference papers, books sections, thesis, magazines and industry reports, this work provides a literature review analyzing PsM origins, definitions, enablers, outputs and emerging trends. Findings: PsM concept evolved in recent years representing a shift from traditional maintenance, leveraging prescriptive analytics, data-driven modeling and optimization techniques to enable proactive decision-making and optimal resource allocation. By harnessing PsM, organizations can anticipate and mitigate failures, optimize maintenance actions and enhance asset reliability. Research limitations/implications: Existing literature points out the following challenges for PsM implementation: prescriptive analytics improvement, scalability of frameworks, development of prototypes, processes integration; PsM maturity assessment; asset health prognostics assertiveness, real-time data availability and adoption of cost functions to grasp business and environmental, social and governance (ESG) costs. Practical implications: Optimal deployment of resources with little or no human intervention in the maintenance decision process and the creation of new services improving reliability and operational performance. Social implications: By optimizing maintenance, not only direct costs diminish but also environmental, social and governance (ESG) related costs decrease by reducing energy waste during equipment's operating phase, assessing the ecological impact of providing maintenance to operators and line maintenance stakeholders and, consequently, minimizing or even eliminating harmful effects on the environment and the human. Originality/value: Work consolidating existing PsM-related knowledge and indicating future work is a gap in the literature. This paper fills this gap. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of the energy efficiency of screw drive and linear drive CNC machine tools in turning operations. Two CNC lathes were investigated, one equipped with screw drives and the other with linear drives, during the turning of specially prepared parts. The research examines active and reactive energy consumption, offering insights into the energy efficiency of different drive technologies. The analysis indicates that lathes with linear drives exhibited a higher reactive power consumption (8 kVar) during idle operation in comparison to those with screw drives (1.2 kVar). However, both drive systems demonstrated comparable potential for reducing reactive power consumption through implementing compensation techniques, with a reduction in reactive power consumption of nearly 70%. For both drive systems, the reduction in power use with compensation was at the level of 23–30% for screw drives and 36–47% for linear drives. The study highlights the importance of considering both active and reactive energy in evaluating the energy efficiency of machine tools. The findings contribute to a deeper understanding of energy consumption in turning processes, aiding in the selection and optimization of drive systems for improved sustainability in manufacturing. Future research should explore tool wear impacts, machine-specific energy optimization, and AI-driven solutions for real-time energy management. [ABSTRACT FROM AUTHOR]

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth. [ABSTRACT FROM AUTHOR]

This paper brieffy overviews some recent and very fresh results on a rather new class of dynamic optimization problems governed by the so-called sweeping (Moreau) processes with controlled moving sets. Uncontrolled sweeping processes have been known in dynamical systems and applications starting from 1970s while control problems for them have drawn attention of mathematicians, applied scientists, and practitioners quite recently. We discuss here such problems and major results achieved in their theory and applications. [ABSTRACT FROM AUTHOR]

To automatic detect and characterize paper impurities with computer vision, we present a novel two parts evaluation procedure with feature representations using Alternating Direction Method of Multipliers (ADMM) sparse codes. The method is based on an offline training step to obtain sparse coefficients and codebooks via learning extracted features with ADMM optimization, followed by an online detection step to use linear SVM classifier to assess defective paper samples from no-defective ones. Our approach bridges the gap between paper impurities evaluation and sparse feature representations, taking advantages of existing ADMM algorithms to handle sparse codes problem. We compare different feature descriptors and sparse code methods to implement the procedure and experimentally validate it on a dataset of 11 paper classes. Experiment results show that the proposed method is competitive and effective in terms of evaluation accuracy and speed. [ABSTRACT FROM AUTHOR]

A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved Grey Wolf Optimization Algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach to multiscale and multiphysics modelling of Solid Oxide Fuel Cells (SOFCs) by combining physics-based simulations with data-driven techniques. The modelling approach is tailored to specific end-use scenarios, ensuring that parameter selection aligns with operational requirements for accurate and efficient SOFC design. The study begins by constructing Representative Volume Elements (RVE s) from reconstructed microstructures, applying first-order homogenisation to upscale material properties, which are then incorporated into a macroscopic SOFC model. A major contribution is the structured model definition based on physical process entities, using a graphical representation of model topology. This approach simplifies complex system interactions by representing capacities (such as reservoirs, distributed systems, and interfaces) and transport processes (e.g., diffusion, convection, thermal diffusion), thereby enhancing clarity and improving the accuracy of SOFC performance simulations. A machine learning framework complements the physics-based modelling by training Artificial Neural Networks (ANNs) on simulation-generated datasets, delivering fast and reliable performance predictions. The study compares two optimisation techniques — Levenberg–Marquardt (LM) and Adam optimiser — demonstrating that LM is more effective for sparse datasets and smaller networks, whereas Adam performs better with large datasets and higher learning capacities. This hybrid modelling approach not only boosts predictive accuracy for SOFC performance but also lowers computational costs. By integrating physics-based simulations, machine learning, and a knowledge-driven simulation platform, this work advances SOFC design and optimisation, contributing to more efficient and cost-effective clean energy solutions. Additionally, the paper introduces a knowledge-driven simulation platform to enhance data management and integrate multiscale, multiphysics models. The platform leverages structured data models and ontological mappings to improve semantic interoperability, allowing for dataset reuse and validation across different simulation stages. This ensures a robust, reusable, and well-organised workflow, facilitating large-scale simulations and improving overall modelling accuracy. • Microstructure modelling links to SOFC properties. • Graphical-based modelling framework enhances clarity of complex SOFC processes. • Finite element analysis with non-isothermal fluid flow. • Machine learning evaluates SOFC performances with LM and Adam optimisers. • Semantic platform streamlines multi-scale modelling and data management. [ABSTRACT FROM AUTHOR]

With rapidly developing new energy technologies, rational energy planning has become an important area of research. Ground source heat pumps (GSHPs) have shown themselves to be highly efficient. effective in reducing building or district energy consumption and operating costs. However, when optimizing integrated energy systems, most studies simplify the GSHP model by using the rated coefficient of performance (COP) of the GSHP unit, neglecting factors such as soil, buried piping, and actual operating conditions. This simplification leads to a deviation from the actual operation of GSHPs, creating a gap between the derived operational guidelines and real-world performance. Therefore, this paper examines a hotel equipped with photovoltaic panels, a GSHP, and a hybrid energy storage unit. By constructing models of the underground pipes, GSHP units, and pumps, this paper considers the thermal exchanger between the underground pipes and the soil, the thermal pump, and the operating status of the unit. The purpose is to optimize the running expenses using an enhanced mote swarm optimization (PSO) algorithm to calculate the optimal operating strategy of system equipment. Compared to the simplified energy system optimization model, the detailed GSHP unit model shows a 21.36% increase in energy consumption, a 13.64% decrease in the mean COP of the GSHP unit, and a 44.4% rise in system running expenses. The differences in the GSHP model affect the energy consumption results of the unit by changing the relationship between the power consumption of the PV system and the GSHP at different times, which in turn affects the operation of the energy storage unit. The final discussion highlights significant differences in the calculated system operating results derived from the two models, suggesting that these may profoundly affect the architectural and enhancement processes of more complex GSHP configurations. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the application of different optimization techniques in the design and production planning phase in the metal additive manufacturing process, specifically laser powder bed fusion (L-PBF) additive technology. This technology has a significant market share and belongs to the group of mature additive technology for the production of end-use metal parts. In the application of this technology, there is a space for additional cost/time reduction by simultaneously optimizing topology structure and part orientations. Simultaneous optimization reduces the production time and, indirectly, the cost of parts production, which is the goal of effective process planning. The novelty in this paper is the comparison of the part orientation solutions defined by the software algorithm and the experienced operator, where the optimal result was selected from the aspect of time and production costs. A feature recognition method together with symmetry operations in the part orientation process were also examined. A framework for the optimal additive manufacturing planning process has been proposed. This framework consists of design and production planning phases, within which there are several other activities: the redesign of the part, topological optimization, the creation of alternative build orientations (ABOs), and, as a final step, the selection of the optimal build orientation (OBO) using the multi-criteria decision method (MCDM). The results obtained after the MCDM hybrid method application clearly indicated that simultaneous topology optimization and part orientation has significant influence on the cost and time of the additive manufacturing process. The paper also proposed a further research direction that should take into consideration the mechanical as well as geometric, dimensioning and tolerances (GDT) characteristics of the part during the process of ABOs and OBO, as well as the uses of symmetry in these fields. [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]

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 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]

The independent rate-distortion optimization technique for the video coding standard H. 266/VVC (versatile video coding) suffers from a loss in coding performance due to the oversight of temporal propagation effects of coding distortion in reference unit. To address this issue, this paper proposed a low-latency rate-distortion optimization algorithm based on the temporal influence of coding distortion. Firstly, leveraging the temporal propagation continuity characteristics of video images, it computed the temporal propagation influence weight of encoding distortion by considering motion compensation prediction errors and reconstruction errors. Secondly, it established a rate-distortion optimization model based on the temporal propagation impact of encoding distortion. Lastly, it utilized the temporal propagation influence weight of encoding distortion to adjust the Lagrange multipliers and quantization parameters of coding units. Experimental results demonstrate that, under low-delay P-frame and B-frame configurations, compared to the baseline algorithm, the BD-rate savings reach 1.6% and 0.9%, and the coding time decrease by 2.93% and 6.02%, respectively. The experimental results demonstrate that the algorithm proposed in this paper effectively improves coding performance without increasing encoding complexity, making it suitable for real-time encoding applications. [ABSTRACT FROM AUTHOR]

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system's topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations. [ABSTRACT FROM AUTHOR]

In recent years, fog computing has become a promising technology that supports computationally intensive and time-sensitive applications, especially when dealing with Internet of Things (IoT) devices with limited processing capability. In this context, offloading can push resource-intensive tasks closer to the end devices at the network edge. This allows user equipment to profit from the fog computing environment by offloading their tasks to fog resources. Thus, computation offloading mechanisms can overcome the resource constraints of devices and enhance the system's performance by minimizing delay and extending the battery lifetime of devices. In this regard, designing an algorithm to decide which tasks to offload and where to execute them is crucial. Recently, there has been a growing interest in utilizing Reinforcement Learning (RL) and deep reinforcement learning (DRL), to address computation offloading mechanisms in the context of fog computing. This paper reviews the research conducted on Reinforcement learning (RL) and Deep Reinforcement Learning (DRL) based computation offloading mechanisms for IoT applications in the fog environment. We provide a comprehensive and detailed survey, analyzing and classifying the research paper in terms of RL techniques, objectives, architecture, and use cases. Then, in particular, we identify the advantages and weaknesses of each paper. After that, We systematically elaborate on open issues and future research directions that are crucial for the next decade. [ABSTRACT FROM AUTHOR]

During the five-axis flank milling process, the uncut chip area varies continuously with the cutter motion, which leads to a challenging issue for the geometric construction of the instantaneous uncut chip thickness (IUCT). In this paper, a novel approach to calculate the IUCT effectively considering the cutter runout effect is proposed. The element motion of the cutter extracted from the cutter location file (CLF) is described with one linear variable and two angular variables, which are necessary factors in the IUCT calculation of five-axis milling. The geometry relationship between cutter motion and IUCT has been analyzed, and the parametric expression of IUCT in five-axis milling can be deduced to predict the cutting force. The explicit five-axis IUCT expression facilitates a more comprehensive understanding of the key parameters in the cutting process, which could be helpful in optimizing the processing parameters. Compared to the numerical method based on true tooth trajectories, the time consumption of the proposed method is reduced greatly in the simulation. The experiment of five-axis flank milling is carried out, and the result reveals that the presented method can be well applied to the practical milling process. We believe that the IUCT model in this paper will promote the simulation and optimization system in five-axis machining. [ABSTRACT FROM AUTHOR]

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with "a priori" and "a posteriori" optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, "interactive" and "Pareto pruning" methods, as well as the consideration of human factors in flexible production systems, remain under-explored. [ABSTRACT FROM AUTHOR]

This review paper provides a comprehensive analysis of radiation shielding concrete, covering its properties, design, evaluation, and applications. It begins with an introduction, stating the objective and scope. The paper explores radiation shielding basics, including ionizing radiation, shielding principles, and materials used for shielding. Concrete's properties relevant to shielding, radiation attenuation mechanisms, and factors affecting its efficiency are discussed. Different types of radiation shielding concrete are examined, along with their applications. The design and formulation of shielding concrete, including mix proportions, optimization techniques, and quality control, are presented. Evaluation methods and standards are discussed. Lastly, challenges, future directions, and emerging technologies are outlined. This review paper serves as a valuable resource for professionals involved in radiation shielding. The review on radiation shielding concrete highlighted its effectiveness in attenuating ionizing radiation, emphasizing material composition, density, and thickness as key design factors. Evaluation methods, such as gamma spectroscopy and Monte Carlo simulations, are discussed, demonstrating its versatile applications in nuclear facilities, healthcare, and space exploration. [ABSTRACT FROM AUTHOR]

The introduction of hybrid alternating current (AC)/direct current (DC) distribution networks led to several developments in smart grid and decentralized power system technology. The paper concentrates on several topics related to the operation of hybrid AC/DC networks. Such as optimization methods, control strategies, energy management, protection issues, and proposed solutions. The implementation of neural network optimization methods has great importance for the successful integration of multiple energy sources, dynamic energy management, establishment of system stability and reliability, power distribution optimization, management of energy storage, and online fault detection and diagnosis in hybrid networks like the hybrid AC–DC microgrids (MG). Taking advantage of renewable energy generation and cost-cutting through the neural network optimization technique holds the key to these progressions. Besides identifying the challenges in the operation of a hybrid system, the paper also compares this system to conventional MGs and shows the benefits of this type of system over different MG structures. This review compares the different topologies, particularly looking at the AC–DC coupled hybrid MGs, and shows the important role of the interlinking of converters that are used for efficient transmission between AC and DC MGs and generally used to implement the different control and optimization techniques. Overall, this review paper can be regarded as a reference, pointing out the pros and cons of integrating hybrid AC/DC distribution networks for future study and improvement paths in this developing area. [ABSTRACT FROM AUTHOR]

The fundamental objective of a modern power system lies in ensuring reliable and effective energy access for its customers. The assessment and determination of optimal operating conditions for power systems involve the utilization of the optimal power flow (OPF) tool. By considering critical factors such as generator power, bus voltages, and line power flow limits while satisfying the power balance equations, the OPF tool enables the identification of the most favorable configuration for efficient power system operation. Traditional optimization methods have limitations in addressing complex power system problems due to poor convergence and long computational times. As a result, computational intelligence tools have gained popularity in recent years. These tools are versatile and enable efficient solution of power system problems by effectively handling qualitative constraints. This paper presents a well-organized and comprehensive review of the algorithms used in power system optimization in the existing literature, encompassing the most recent developments in the field. Specifically, it examines the application of various population-based artificial intelligence techniques that have gained widespread adoption over the past decade (2012–2022). The aim of these techniques is to resolve an OPF problem. This paper organizes the reviewed papers into various types of population-based metaheuristic algorithms, each one implemented sequentially to deal with the OPF problem in the same chronological order in which they appeared in the literature. [ABSTRACT FROM AUTHOR]

In the Internet era, the e‐commerce industry has risen, its development scale continues to expand, cross‐border e‐commerce (CBEC) has also been born, and it is now in the stage of sustainable development. The rapid development of CBEC also needs the strong support of logistics, the two are inseparable, and today, the development scale of CBEC is constantly expanding. The existing e‐commerce logistics (ECL) model is also gradually unable to meet the increasingly diverse needs of users, and new logistics models need to be actively explored. To change this situation, this paper carried out a specific analysis of CBEC logistics model, and applied embedded technology to ECL, which also built a logistics tracking system. At the same time, combined with the ant colony algorithm, the paper carried out experimental research on the logistics package distribution route planning problem. From the experimental results, in terms of average delivery time, the algorithm's result was 25.95 hr, while the traditional algorithm was 32.53 hr; in terms of average distribution freight cost, the algorithm's result was 163.3 yuan, while the traditional algorithm was 257.7 yuan; in terms of average distribution cost, this algorithm's result was 131.53 yuan, while the traditional algorithm was 211.68 yuan. To sum up, this algorithm could effectively optimize the distribution route of logistics packages and improve the efficiency of package transportation. [ABSTRACT FROM AUTHOR]

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs. [ABSTRACT FROM AUTHOR]

This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor λ. The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%. [ABSTRACT FROM AUTHOR]

The scientific and technical aspects of gun barrel movement for effective attack and defense are of ultimate significance. The precise control of the gun barrel is of strategic importance during targeting, especially in changing environmental conditions. Fuzzy logic control offers a powerful alternative to classical and manual solutions to deal with the complexity and uncertainties of dynamic systems, thus increasing targeting accuracy and precision. This approach provides adaptability and intuitive parameterization while simplifying system design and implementation. On the other hand, in fuzzy control management, accurate modeling and visualization of gun barrel movement is essential to achieve efficient aiming and performance. Many papers and realizations can be found on the (automatic) fuzzy control of cannon barrels. In this paper, the authors also suggest an implementation of a fuzzy-controlled cannon barrel. The novelty of this approach is the application of new defuzzification methods, resulting in an accurate solution for the problem. The article starts with a review of the theory and literature on the control of cannon barrels. It is followed by a comparison of different implementations, including simulation tests on the accuracy, and a discussion of some practical issues. [ABSTRACT FROM AUTHOR]

This paper focuses on the computation issue of portfolio optimisation with scenario-based mean-Average Value at Risk (AVaR) in credibilistic environment. The portfolio optimisation problem is designed in two cases: risk taker model and risk-averse model. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimisation theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with fuzzy returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

The shortest path problem is fundamental in graph theory and has been studied extensively due to its practical importance. Despite this aspect, finding the shortest path between two nodes remains a significant challenge in many applications, as it often becomes complex and time consuming. This complexity becomes even more challenging when constraints make the problem non-additive, thereby increasing the difficulty of finding the optimal path. The objective of this paper is to present a broad perspective on the conventional shortest path problem. It introduces a new method to classify cost functions associated with graphs by defining distinct sets of cost functions. This classification facilitates the exploration of line graphs and an understanding of the upper bounds on the transformation sizes for these types of graphs. Based on these foundations, the paper proposes a practical methodology for solving non-additive shortest path problems. It also provides a proof of optimality and establishes an upper bound on the algorithmic cost of the proposed methodology. This study not only expands the scope of traditional shortest path problems but also highlights their computational complexity and potential solutions. [ABSTRACT FROM AUTHOR]

The document is a special issue of the Mathematics journal focusing on "Optimization Algorithms: Theory and Applications." It includes 10 selected papers from 9 countries covering topics such as arithmetic optimization, semi-parametric regression, flow shop scheduling, unconstrained optimization, metaheuristic techniques, many-objective optimization, and more. The papers present innovative algorithms and approaches that outperform existing methods in various optimization problems, showcasing the diverse research fields within the optimization area. [Extracted from the article]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young's modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy. [ABSTRACT FROM AUTHOR]

Stochastic Block Models (SBMs) have emerged as a powerful framework for modelling community structures in networks. However, accurately determining the number of communities in SBMs remains challenging. In this paper, we propose a novel approach for community detection in stochastic block models through penalized variational estimation. Our method introduces two penalties on the variational objective function to ensure elimination of redundant blocks. We present the variational computing procedure, and demonstrate through simulation and empirical analysis that our approach can effectively identifies community structures and achieves accurate parameter estimation simultaneously. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed optimisation problem in an iterative environment, where the global objective function consists of agents' local objective functions, and each agent with the local objective function performs repeated tasks in finite time. The objective is to minimise the global objective function by the local communication of agents in the repeated running system. To solve this problem, we propose a distributed optimisation algorithm based on iterative learning methods that combines the terminal iterative learning strategy with the subgradient strategy. When the initial states of all agents are the same in each iteration, by the proposed algorithm, it is proved that all agents' states asymptotically converge to the optimal solution. Moreover, considering that the initial states of agents in each iteration may not be accurately measured, we further study the distributed optimisation problem under different initial states. We find that all agents' states asymptotically converge to the neighbourhood of the optimal solution. Finally, the effectiveness of the algorithm is verified by the numerical simulations. [ABSTRACT FROM AUTHOR]

Standard quadratic optimization problems (StQPs) provide a versatile modelling tool in various applications. In this paper, we consider StQPs with a hard sparsity constraint, referred to as sparse StQPs. We focus on various tractable convex relaxations of sparse StQPs arising from a mixed-binary quadratic formulation, namely, the linear optimization relaxation given by the reformulation–linearization technique, the Shor relaxation, and the relaxation resulting from their combination. We establish several structural properties of these relaxations in relation to the corresponding relaxations of StQPs without any sparsity constraints, and pay particular attention to the rank-one feasible solutions retained by these relaxations. We then utilize these relations to establish several results about the quality of the lower bounds arising from different relaxations. We also present several conditions that ensure the exactness of each relaxation. [ABSTRACT FROM AUTHOR]

Microfluidic technology, which involves the manipulation of fluids in microchannels, faces challenges in channel design and performance optimization due to its complex, multi-parameter nature. Traditional design and optimization approaches usually rely on time-consuming numerical simulations, or on trial-and-error methods, which entail high costs associated with experimental evaluations. Additionally, commonly used optimization methods require many numerical simulations, and to avoid excessive computation time, they approximate simulation results with faster surrogate models. Alternatively, machine learning (ML) is becoming increasingly significant in microfluidics and technology in general, enabling advancements in data analysis, automation, and system optimization. Among ML methods, Bayesian optimization (BO) stands out by systematically exploring the design space, usually using Gaussian processes (GP) to model the objective function and guide the search for optimal designs. In this paper, we demonstrate the application of BO in the design optimization of the microfluidic systems, by enhancing the mixing performance of a micromixer with parallelogram barriers and a Tesla micromixer modified with parallelogram barriers. Micromixer models were made using Comsol Multiphysics software® and their geometric parameters were optimized using BO. The presented approach minimizes the number of required simulations to reach the optimal design, thus eliminating the need for developing a separate surrogate model for approximation of the simulation results. The results showed the effectiveness of using BO for design optimization, both in terms of the execution speed and reaching the optimum of the objective function. The optimal geometries for efficient mixing were achieved at least an order of magnitude faster compared to state-of-the-art optimization methods for microfluidic design. In addition, the presented approach can be widely applied to other microfluidic devices, such as droplet generators, particle separators, etc. [ABSTRACT FROM AUTHOR]

The large-scale integration of renewable energy sources and new loads, such as distributed photovoltaics and electric vehicles, has resulted in frequent power quality issues within distribution networks. Traditional AC distribution networks lack the necessary flexibility and have limited capacity to accommodate these new energy sources and loads. Transforming the conventional distribution network into an AC-DC hybrid network using flexible interconnection devices like Voltage Source Converters can enhance the network's flexibility, mitigating the power quality challenges arising from the integration of renewable energy and new loads. Electric buses, with their substantial capacity, mobility, and centralized management, offer potential as mobile energy storage. They can participate in the dispatching of the distribution network, thereby improving the network's flexibility in power regulation. This paper proposes a coordinated optimization approach that integrates electric buses and VSCs for distribution network dispatch. This method enables electric buses to assist in power dispatch without interfering with their primary public transport duties, thus enhancing the network's capacity to absorb new energy sources and loads. Firstly, considering the mobility characteristics of electric buses, a multi-layer stochastic Time–Space Network model is developed for bus dispatching. Secondly, an optimization model is constructed that accounts for the coordination of charging and discharging power between VSCs and electric buses, with the objective of minimizing the network losses in the distribution system. Finally, the proposed model is transformed into a second-order cone programming formulation, facilitating its solution through convex optimization techniques. The effectiveness of the proposed approach is demonstrated through a case study. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern power systems (MPSs), driven by the integration of renewable energy sources and multi-area configurations, demands robust and adaptive load frequency control (LFC) strategies. This paper proposes a novel approach to the LFC of the MPS by integrating a proportional–integral–derivative (PID) controller optimized using the gray wolf optimizer (GWO) algorithm. The effectiveness of the GWO-PID method is evaluated on multi-area power systems, including systems integrated with wind energy. The GWO-PID controller shows superior frequency stability, achieving deviations of 49.67 Hz, 49.68 Hz, 49.87 Hz, 49.87 Hz and 49.88 Hz for area 1 and area 2 of the two-area multisource MPS, as well as for area 1, area 2 and area 3 in the three-area multisource MPS. The results demonstrate significant improvements in frequency stabilization, reduced oscillations and enhanced steady-state accuracy compared to traditional optimization techniques. This study emphasizes the scalability and adaptability of the proposed method to changing load conditions and complexity of the MPSs, providing a potential solution to ensure stability and reliability for the MPSs. [ABSTRACT FROM AUTHOR]