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The growing global demand for clean, reliable, and affordable energy presents a critical challenge, especially with the need to reduce reliance on fossil fuels and minimize environmental impact. This article proposes a hybrid biomass and geothermal system to address these needs, providing a sustainable solution for hydrogen and electricity production while reducing peak demand. The system incorporates an ammonia synthesis cycle that captures nitrogen from the atmosphere, producing ammonia as a carbon-free energy source and a flexible energy storage alternative to costly, environmentally hazardous batteries. By applying a machine learning-optimized grey wolf algorithm, the system achieves 546.1 kg/day of ammonia production, 3224 kW of net power output, 43.7% energy efficiency, and a low Levelized Cost of Energy (LCOE) of 65.7 USD/MWh, with emissions of 130.9 g/kWh. Optimization further improves efficiency to 44.1%, reduces emissions to 127.1 g/kWh, and lowers costs to 63.4 USD/MWh. Exergy analysis identifies major areas of energy loss, offering pathways for future improvement. [Display omitted] • A novel hybrid system based on biomass and geothermal hybridization is introduced. • The system is integrated with the ammonia cycle driven by green hydrogen. • Machine learning-aided optimal energy management is proposed via Grey Wolf method. • The system generates 546 kg of ammonia daily as a promising energy career/storage. • Optimization achieves 2.3 USD/MWh and 3.8 g/kWh lower cost and emission. [ABSTRACT FROM AUTHOR]

To enhance the operational efficiency of hydrogen compressors and reduce the costs associated with the storage and transportation of hydrogen, this paper has developed a novel capacity control system (CCS). This system ensures that the compressor accurately compresses hydrogen according to actual demand, driven by an electromagnetic actuator, which simplifies the system design and enhances operational precision. To optimize the system's performance, a multi-objective optimization was conducted. Initially, a hydrogen compressor backflow working model and a comprehensive evaluation index system were established. Subsequently, a three-tier computational multi-objective optimization framework was proposed based on the working model, ultimately determining the optimal combination of key system parameters. A prototype was manufactured and subjected to validation testing and a full life cycle analysis. The results indicate that the developed system can accurately regulate the amount of compressed hydrogen within a range of 0%–100% load. Compared to traditional systems, for the same compressor group, carbon emissions and costs were reduced by 41.93% and 36.49%, respectively. The optimized design of the internal and external resistance angles and the limiter plate thickness are 11°, 16°, and 1 mm, respectively. Compared to the baseline state, the specific energy consumption of the hydrogen compressor per unit of exhaust volume has been reduced by 28.22%. Additionally, during the ejection and retraction processes of the actuator, the peak vibration impact on the hydrogen compressor has been reduced by 77.6% and 73.3%, respectively, achieving optimal comprehensive performance. • Developed a new CCS to enhance the operational efficiency of hydrogen compressors. • Established a backflow working model and a comprehensive evaluation index system. • Proposed a framework for multi-objective optimization. • Fabricated and tested an experimental setup for validation. [ABSTRACT FROM AUTHOR]

This research aims to establish a systematic framework for parametric identification of materials undergoing high temperatures and high strain rates. While advanced testing equipment, such as the Gleeble physical simulator, can produce controlled measurements of specimens under various conditions, significant challenges remain in determining the parameters of constitutive relations. Temperature gradients inevitably arise during Gleeble testing, leading to nonuniform strain distribution caused by complex thermal–mechanical coupling. Although finite element analysis of Gleeble testing can be performed, such simulations are computationally expensive, making brute-force optimization to minimize the difference between experimental data and finite element simulation across the parametric space infeasible. Furthermore, since the related constitutive relations are semi-empirical in nature, the ground truth of the constitutive parameters is generally unknown. In this context, a single-objective optimization based on a number of testing conditions may yield biased results or become trapped in local minima. In this research, we employ finite element analysis simulating Gleeble operation as the foundation, leveraging a suite of computational intelligence tools to address these challenges. We first develop a multi-response Gaussian process surrogate model, trained using a relatively small amount of finite element data, to rapidly emulate the forward analysis. We then implement a multi-objective optimization approach using simulated annealing to individually minimize the differences between experimental results and emulations under various testing conditions. AISI 9310 steel and the Johnson–Cook model are adopted for methodological demonstration. The development of the finite element model, Gaussian process surrogate model, and inverse optimization is detailed, and the results obtained are discussed. This framework can be extended to the parametric identification of other materials and heat treatment conditions using Gleeble testing. [ABSTRACT FROM AUTHOR]

The machining trajectory of the irregular contour is usually discretized into straight lines and arcs, and process parameters selection affects the quality and efficiency of irregular sheet metal parts machining. To guide parameters selection of irregular sheet metal parts milling, a multi-objective optimization framework for efficiency and side machining quality is constructed. In the framework, to improve the modeling accuracy and reduce modeling cost, the theory-data coupled models of side roughness for straight line, convex arc and concave arc constructed, respectively. Aiming at the problems of single search method and susceptibility to local optima in the standard multi-objective seagull optimization algorithm (MOSOA), an improved MOSOA (MOSOAimprove) is proposed to solved the multi-objective optimization model of side quality and efficiency developed by the coupled model of side roughness and the empirical formula of the material removal rate. The effectiveness of coupled models and MOSOAimprove in multi-objective optimization of irregular sheet metal parts milling are verified by the actual machining. [ABSTRACT FROM AUTHOR]

The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding material consumption and shielding gas consumption. Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. The grey wolf multi-wandering strategy (first) is proposed to enhance the population diversity. The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. A welding workshop green scheduling case is designed to verify the model and algorithm proposed in this paper. The minimum completion time and carbon emissions obtained by the IGWO algorithm are 842.14 and 3.85E + 05, respectively. This result is better than that obtained by NSGA-II and GWO.The results show that the model effectively reduce the carbon emissions of the workshop, and the algorithm can effectively solve the model. [ABSTRACT FROM AUTHOR]

This paper proposes a proton exchange membrane fuel cell (PEMFC) based combined heat and power (CHP) system model, comprising a PEMFC aging model, a battery aging model, and subcomponents model. In low temperature environments, heat production of PEMFC stack is utilized to provide auxiliary heating for lithium-ion storage battery, greatly slowing down its capacity degradation rate. Several parameters' (PEMFC operating current, PEMFC temperature, battery temperature and battery initial capacity) effects on lifetime and efficiency of entire system are investigated. To improve system lifetime and efficiency, these parameters are optimized using non-dominated sorting genetic algorithms (NSGA-Ⅱ) and the optimal solution is selected by technique for order preference by similarity to ideal solution (TOPSIS). The results show that the auxiliary heating for battery not only increases the system lifetime by 5.15%, but also improves the energy utilization of the PEMFC-based CHP system by 6.53% compared with system without auxiliary heating for battery. Under initial battery capacity of 40 Ah, the optimized parameters can further extend system life by 4.87% and increase efficiency by 4.48%, with optimized lifetime of 7749 h and efficiency of 80.44%. The optimized PEMFC stack's power is increased by 42 W with current density of 0.76 A/cm2, which prolongs its aging time and stabilizes battery temperature at 25.09 °C. The present study provides guidance for the enhancement of PEMFC-CHP system's lifetime and efficiency. • Combined heat and power system with fuel cell and battery aging model is proposed. • Heat from PEM fuel cell is used to provide auxiliary heating for battery. • Auxiliary heating increases system lifetime by 5.15% and efficiency by 6.53%. • Operating parameters are optimized to improve lifetime and efficiency of system. [ABSTRACT FROM AUTHOR]

Pointing mechanism is widely used in aerospace field, and its pointing accuracy and stability have high requirements. The pointing mechanism will be affected by external interference when it works. In order to eliminate the impact of interference forces on the output accuracy of the mechanism, firstly, this paper proposes a design method for high-precision pointing mechanisms based on interference separation, aiming at the high-precision pointing requirements of pointing mechanisms. Based on the screw theory, a synthesis method for inner compensation mechanisms has been proposed. And a new type of double-layer parallel mechanism has been designed to compensate for interference forces. Then, the kinematics and dynamics of the mechanism are carried out. An evaluation index for compensating external interference forces is proposed. The interference compensation analysis is conducted for the pointing mechanism. The correctness of the proposed interference force compensation coefficient is verified. Finally, in order to find the optimal solution for the workspace and interference force compensation coefficient of the pointing mechanism, multi-objective optimization design of the structural parameters of the mechanism was carried out based on the particle swarm optimization algorithm. This provides a theoretical basis for the prototype design of the subsequent double-layer parallel mechanism. This double-layer parallel mechanism combines the advantages of large load-bearing capacity, large workspace, and high output accuracy. It can be better applied in the aerospace field where high-precision pointing and force interference compensation are integrated. [ABSTRACT FROM AUTHOR]

Helical milling is widely used in aerospace as a key processing technology for Carbon fiber reinforced polymer (CFRP). However, the eccentric machining characteristics lead to an unusually complex pattern of cutting force and residual stress distribution on the work-piece during helical milling processing. Based on the Hashin failure criterion, a 3D FEM model of CFRP helical milling was built for analyzing the changing law of cutting force, then the three factors and three levels orthogonal tests were used to investigate the influence of machining parameters on the axial force, radial force, and minimum principal residual stress, finally, the multi-objective optimization based on grey correlation analysis was realized. The results showed that the errors of axial force and radial force obtained by simulation and experiment were 10.68% and 12.26%, respectively. The axial force and radial force were negatively correlated to the spindle speed, positively correlated to the axial cutting depth, and uncorrelated to the feed per tooth. The minimum principal residual stress was negatively correlated to the spindle speed, positively correlated to the feed per tooth, and uncorrelated to the axial cutting depth. The degree of influence on optimization of machining parameters was: spindle speed > axial cutting depth > feed per tooth. The corresponding average grey correlation degree differences were 0.280981, 0.216859, and 0.013422, respectively. The maximum value of grey correlation degree in the orthogonal test was 0.874372, and the corresponding optimal parameters combination was the spindle speed 8000 r/min, feed per tooth 0.03 mm/z, and axial cutting depth 0.2 mm/r. [ABSTRACT FROM AUTHOR]

The aircraft landing problem (ALP) is a challenging scheduling and optimization problem in the industry and engineering, which has attracted attention in recent decades. Existing research has predominantly concentrated on optimizing aircraft delay and the financial implications of early or late landings. However, given the paramount significance of airport fuel costs at airports and the critical need for efficient fuel utilization, we aim to minimize airplane fuel consumption by streamlining operational time. In this paper, we present an innovative model with two main objectives: minimizing airplane fuel consumption by reducing dwell time and minimizing cost operation. To address these dual objectives concurrently, we propose a new method known as the multi populations of multiple objectives (MPMO) framework, which is modeled through a non-dominated sorting genetic algorithm-II (NSGA-II) called MPNSGA-II. First, MPNSGA-II employs two separate populations to optimize each objective. Second, to prevent populations from fixating solely on their respective single objectives, MPNSGA-II introduces an archive sharing strategy (ASS). This technique stores elite solutions gathered from two populations. Additionally, we introduce an archive update strategy (AUS) to enhance the quality of solutions stored in the archive. The proposed algorithm has been compared with other well-known algorithms, NSGA-II, multi-objective particle swarm optimization (MOPSO), and NSGA-III. The proposed algorithm shows a cost reduction in 18.01%, 16.75%, and 15.21%. Statistical precision, underscored through the application of the nonparametric Friedman test, corroborates the supremacy of the proposed method, clinching the highest ranking compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design. [ABSTRACT FROM AUTHOR]

Low-impact development (LID) practices are critical for mitigating urban stormwater runoff and alleviating flood risks. The strategic placement of LID facilities is paramount to optimizing their efficacy within urban landscapes. This study conducts a comprehensive bibliometric analysis of LID-related literature over the past decade, utilizing data visualization tools to elucidate key disciplines, publication trends, and the prevalence of various optimization algorithms. We delve into the application of multi-objective optimization (MOO) algorithms in LID facility layout, examining their practical applications, theoretical underpinnings, and case studies. The paper also scrutinizes the strengths and limitations of these algorithms, proposing future research trajectories that leverage MOO to enhance LID's role in urban stormwater management. [ABSTRACT FROM AUTHOR]

Gear is one of the most crucial components of the transmission system, and the performance of gear directly affects the efficiency and reliability of the transmission system. Conventional methods for designing gear parameters involve several time-consuming and complex steps, which may not guarantee optimal performance. Therefore, we propose a new method for designing gear parameters that aims to improve efficiency and accuracy. First, the tooth surface equations of spur and helical involute gears suitable for symmetric and asymmetric teeth are deduced based on the gear-forming machining principle. Second, the performance evaluation models for load capacity, dynamic performance, efficiency, and power density of the gears are established based on the precise gear surface. The design objectives are standardized and evaluated comprehensively using a linear weighting method. Finally, a forward performance-driven design method of gear parameters is established. The proposed method is applied to a helical gear pair design case, and the results show that 90.7% of the individuals in the Pareto optimal front are asymmetric gears, with 9.3% being symmetric gears. This suggests that asymmetric gears have more opportunities to be optimal than symmetric gears. The highest-ranked gear designed using the proposed method is superior to the gear designed using conventional methods. [ABSTRACT FROM AUTHOR]

Machinery operation is a major source of carbon emissions in building deconstruction. Early intervention through Design for Deconstruction (DfD) is crucial for emission reduction, yet the factors influencing these emissions are underexplored. This study integrates parametric BIM with multi-objective optimization (MOO) to assess mechanical carbon emissions in deconstruction. Using the Octopus solver in Grasshopper for Rhino, the study analyzes independent variables—possible working hours (PWH), vertical speed (VS), and horizontal speed (HS)—and dependent variables—minimum mechanical carbon emissions (MCE (min)), minimum deconstruction period (DP (min)), and maximum working efficiency (WE (max)). A lightweight steel roof truss structure is analyzed, comparing real-world deconstruction with optimized DfD schemes. Sensitivity analysis for BIM-MOO optimized results reveal that: (1) Adjusting PWH, VS, and HS significantly affects WE and DP, though with limited impact on carbon emissions; (2) VS influences WE and DP more than HS; (3) Limiting DP is essential for balancing WE, DP, and MCE, with WE adjusted to 20–60% and modifications to PWH and VS achieving balanced management. This study underscores the importance of early design and real-time adjustments for efficient, low-emission deconstruction, supporting the advancement of green building practices. [ABSTRACT FROM AUTHOR]

For multi‐enzymatic cases, the determination of the batch reactor (BR) optimal operating policy often translates into a difficult multi‐objective problem. Exemplification is made here for the enzymatic reduction of D‐fructose to mannitol by using the mannitol dehydrogenase (MDH) enzyme and nicotinamide adenine dinucleotide (NADH) cofactor, with in situ regeneration of NADH at the expense of formate degradation by using the FDH enzyme. This paper presents an original rule to in silico generate the problem solution, by using the Pareto optimal‐front approach with accounting for pairs of competing economic goals and constraints. The optimal BR is then compared to an optimal fed‐BR (FBR), or a series of equal BRs (SeqBR). As proved, the Pareto‐optimal front alternative is an advantageous option, compared to the classical nonlinear programming technique, being simple to apply, by considering pairs of opposite objective functions. In the present case study, the Pareto‐optimal BR operating mode predicts an M‐productivity 1.5x better than those of an optimized FBR, with comparable enzymes consumption. The MDH consumption of this Pareto‐optimal BR is 10x smaller than an optimal SeqBR, and 130x smaller vs. heuristic (sub)optimal BR. [ABSTRACT FROM AUTHOR]

ABSTRACT The recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model. [ABSTRACT FROM AUTHOR]

In the context of multi-objective optimization, a properly efficient solution is one that is efficient while at least one of the tradeoffs between different objectives is limited. However, in some situations, it is possible that all efficient solutions have unlimited tradeoffs, which is not always appropriate. To provide new solutions that have at least one bounded tradeoff for such problems, we first introduce a new concept of efficiency by applying a family of scalarization functions for the decomposed multi-objective optimization problem. Then, we extend the concept of proper efficiency by examining the boundedness of the tradeoffs between scalar optimization subproblems and other subproblems. Another purpose of this paper is to investigate the effect of using the scalarizing functions for some subproblems in the decomposed multi-objective optimization problem. Our findings suggest that as the number of subproblems that use scalarizing functions increases, the solution set associated with the generalized proper efficiency concept becomes smaller. [ABSTRACT FROM AUTHOR]

AbstractVehicle crashworthiness is a critical aspect of the passive safety domain in passenger cars, and crash boxes play a significant role in vehicle collisions. Currently, the crash boxes predominantly utilized in vehicles are primarily simple thin-walled structures, which exhibit average energy-absorbing capabilities. To enhance vehicle collision safety, this article proposes an inner core filled with negative Poisson’s ratio (NPR) structure and polyurethane (PU) material to the design of crash box. Initially, a double-arrow type NPR structure is selected as the framework, with polyurethane (PU) serving as the filling material. This combination forms the inner core of the crash box. A collision analysis is conducted on three types of crash boxes, examining the differences in their performance indicators in detail to demonstrate the superiority of the proposed design. Subsequently, variables that significantly influence the evaluation metrics were identified through extreme value difference analysis, and these variables were designated as the design parameters for the subsequent optimization process. Finally, the Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were employed as optimization algorithms for the optimal design, and the optimal results of the two algorithms are determined separately using Normal Boundary Intersection (NBI) method, and then compared to determine the overall optimal solution. The simulation results indicate that the NSGA-II optimized NPR/PU collision box provides substantial advantages in overall performance. After NSGA-II optimization, the NPR/PU crash box exhibits reduced overall collapse displacement and maximum peak collision force compared to other crash boxes, along with enhanced specific energy absorption capacity. These findings indicate that the designed NPR/PU crash box significantly improves the vehicle’s crashworthiness in the event of a collision. This article offers valuable theoretical insights to support the development and exploration of automotive crash boxes. [ABSTRACT FROM AUTHOR]

This study aims to investigate the multi-objective optimization method for liquid cooling plates in automotive power batteries. The response surface method and NSGA-II were combined to optimize the temperature of the battery system under liquid-cooled conditions and the internal pressure of the liquid-cooled plate. The optimal Latin hypercube sampling method was used for sampling, with the flow channel parameters of the liquid-cooled plate and the cooling fluid inlet flow rate as design variables and the maximum temperature of the battery system and the maximum internal pressure of the liquid-cooled plate as target functions. The response surface model was fitted, and the Pareto solution set for the target to be optimized was obtained using NSGA-II. The LINMAP decision-making algorithm was employed to obtain the optimal solution, which is a maximum temperature of 37.25 °C for the battery and a maximum pressure of 63.3 Pa for the liquid-cooled plate. [ABSTRACT FROM AUTHOR]

The polymer polyetheretherketone (PEEK) is gradually being used in dental restorations because of its excellent mechanical properties, chemical resistance, fatigue resistance, thermal stability, radiation translucency and good biocompatibility. To process PEEK dentures with lower surface roughness as quickly as possible, the non-dominated sorting genetic algorithm-II (NSGA-II) integrated genetic algorithm back propagation (GABP) neural network was proposed, which can adjust the combination of process parameters for milling PEEK dentures. The PEEK machining was conducted using a four-axis dental milling machine at different process parameters. The surface roughness of PEEK dentures was characterized using surface roughness profiler and scanning electron microscopy (SEM). The optimum machining performance of milling PEEK dentures was investigated using a multi-objective optimization model named as NSGA-II integrated GABP neural network algorithm. The surface roughness (Ra) and material removal rate (MRR) were used as optimization objectives. The multi-objective optimization model effectively improved surface roughness and machining efficiency for milling PEEK dentures. The validation experiments showed that the surface roughness of all PEEK dentures was less than 0.2 μ m , which was within the range of surface roughness set in this paper. The GABP surface roughness prediction model had an average error of 6 %. For the same surface roughness value, the optimized milling parameters all had a greater material removal rate. The research results can improve current PEEK denture CAD/CAM technology by providing appropriate milling parameters using NSGA-II integrated GABP algorithm. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic power stations into large-capacity hydropower stations is an efficient and promising method for regulating large-scale photovoltaic power generation. However, constrained by the time step length, traditional long-term scheduling of hydro-PV hybrid systems does not adequately consider short-term operational performance indicators, resulting in sub-optimal scheduling plans that fail to coordinate the consumption of photovoltaic power and the utilization of water resources in the basin. To address this, this study established a long-term optimal scheduling model for hydro-PV hybrid systems. This model overcomes the limitation of the time step length in long-term scheduling by incorporating long-term power generation goals and short-term operation performance targets into the long-term optimal scheduling process based on scheduling auxiliary functions. In case studies, the optimised model balanced the long-term power-generation goals and short-term operational performance targets by redistributing energy across different periods. Compared to optimization models that did not consider short-term operation performance, in a typical normal year, the model effectively reduced the electricity curtailment volume (28.54%) and power shortage volume (10.91%) of the hybrid system while increasing on-grid electricity (0.03%). Similar improvements were observed in wet and dry years. These findings provide decision support for hydropower scheduling in the context of large-scale photovoltaic power integration. [ABSTRACT FROM AUTHOR]

The increasing global demand for energy-efficient cooling systems, combined with the need to reduce greenhouse gas emissions, has led to growing interest in using low-GWP (global warming potential) refrigerants. This study conducts a multi-objective optimization of a small-scale organic Rankine cycle–vapor compression cycle (ORC-VCC) system, utilizing refrigerants R1233zd, R1244yd, and R1336mzz, both individually and in combination within ORC and VCC systems. The optimization was performed for nine distinct cases, with the goals of maximizing the coefficient of performance (COP), maximizing cooling power, and minimizing the pressure ratio in the compressor to enhance efficiency, cooling capacity, and mechanical reliability. The optimization employed the Non-dominated Sorting Genetic Algorithm III (NSGA-III), a robust multi-objective optimization technique that is well-suited for exploring complex, non-linear solution spaces. This approach effectively navigated trade-offs between competing objectives and identified optimal system configurations. Using this multi-objective approach, the system achieved a COP of 0.57, a pressure ratio around 3, and a cooling capacity exceeding 33 kW under the specified boundary conditions, leading to improved mechanical reliability, system simplicity, and longevity. Additionally, the system was optimized for operation with a cooling water temperature of 25 °C, reflecting realistic conditions for contemporary cooling applications. [ABSTRACT FROM AUTHOR]

A two-dimensional (2D) piston electro-hydraulic pump has been proposed further to enhance the power density of the electro-hydraulic pump. The 2D piston pump, characterized by high power density and a slender shape, is embedded within the stator of the motor in a co-rotor configuration where the piston and the motor's rotor are in tandem. The intimate design of the hydraulic pump and the motor results in a coupling between the two, with intricate relationships and influences existing between the geometric parameters of the piston pump and the dimensions of the motor's rotor. Based on the operational requirements and structure of the 2D piston pump, a permanent magnet synchronous motor (PMSM) designed for use with a 2D piston electro-hydraulic pump is developed. This study examines the impact of the motor's stator iron core geometric parameters on both the electromagnetic and mechanical properties of a PMSM and completes the necessary performance validations. The optimization objectives of the motor are determined through an analysis of the influence of the key parameters of the rotor and stator on torque, torque ripple, and motor loss. A surrogate optimization model is constructed using a metamodel of optimal prognosis (MOP) to optimize the torque, torque ripple, and motor loss. Evolutionary genetic algorithms are utilized to achieve the multi-objective optimization design. A finite element simulation is used to compare the electromagnetic performance of the initial motor and optimal motor. Based on the optimal motor parameters, a 2.5 kW motor prototype is manufactured, and the experimental results validate the feasibility and effectiveness of the motor design and optimization. [ABSTRACT FROM AUTHOR]

The microsensor is modeled as a two-layer micro beam in which the first layer is quartz and the second layer is silicon. This beam is clamped-free and beam has a rectangular cross-section. Sensitivity and quality factors which are two important functions in sensors are selected as objective functions. Dimensions of the micro beam are design variables, so this optimization has two functions and four variables of the design. Objective functions are depended on the natural frequency, material properties, and dimensions of the beam. The natural frequency is obtained according to the modified couple stress theory by using Rayleigh's method. Optimization is done by using genetic algorithm. Results show the length of the beam has a significant effect on the sensitivity and quality factor. Also, it can be seen that when dimensions of the beam decrease, sensitivity increases, and quality factor decreases. Results are presented based on non-classical and the classical theories. [ABSTRACT FROM AUTHOR]

Crushing behavior analysis and energy absorption optimization are crucial for lightweight structures in automotive applications. The present paper aims to investigate the crushing behavior of thin-walled aluminum/CFRP hybrid tubes under axial loading using an explicit finite element (FE) simulation. The damage constitutive models of aluminum and CFRP are implemented by coding the user-defined subroutine VUMAT in ABAQUS/Explicit, which includes the damage initiation and evolution laws and element deletion scheme. Parametric studies are conducted to assess the effects of radius and aluminum layer thickness on the crushing performance of hybrid tubes. Additionally, a multi-objective optimization is performed on the Isight platform using a non-dominant sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS) with entropy weight method. The optimization aims to maximize crashworthiness and increase energy absorption capacity, enabling designers to select an optimum size ratio. [ABSTRACT FROM AUTHOR]

This paper focuses on the difficulties in high-quality and high-efficiency micro-milling nickel-based superalloy micro thin-walled parts. The second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is improved. A central composite experiment is designed, and a surface roughness prediction model is developed for micro-milling thin-walled parts. A prediction model for surface residual stress on thin-walled parts is developed using an L9(34) orthogonal simulation experiment. Using the NSGA-II algorithm, the four cutting parameters (spindle speed, feed per tooth, axial cutting depth, and radial cutting depth) are optimized to achieve low surface roughness and high material removal rate, while stable cutting and surface compressive residual stress are considered constraints. Finally, the high-quality and high-efficiency micro-milling of the Inconel 718 cross-shaped thin-walled parts is realized. [ABSTRACT FROM AUTHOR]

• A novel tubular indirect evaporative cooler is proposed. • A theoretical model is developed and experimentally validated. • Regression models for air treatment performance prediction are established. • Multi-objective optimization with relative weights of output responses is obtained. • The regression models can predict the cooler's performance in diverse climates. Indirect evaporative cooling (IEC) technology is an energy-efficient approach for regulating the indoor thermal environment of buildings. The conventional tubular indirect evaporative cooler (TIEC) may have a relatively low cooling efficiency due to poor wettability issues. The application of moisture-conducting fibers provides a feasible way to solve the above problem. However, the integration of moisture-conducting fibers with TIEC is still in the exploratory stage. This study proposed a novel moisture-conducting fiber-assisted TIEC and conducted a multi-objective optimization. An experimental facility and theoretical model of the proposed moisture-conducting fiber-assisted TIEC were developed. Based on the numerical model validated by experiments and response surface methodology (RSM), the regression models for performance prediction of the cooler were established. Eight input parameters including inlet air parameters, operating parameters and geometric parameters were selected, and four performance evaluation indicators were chosen as output responses. The parameter sensitivity of the regression models was analyzed. The multi-objective optimization was performed by considering the influence of different relative weights assigned to the output responses. Furthermore, the performance of the optimized cooler applied in different climate zones was predicted. The results showed that the product air temperature drop could achieve 8.8–11.3 °C after cooling by the cooler. The established regression models can predict the performance of the moisture-conducting fiber-assisted TIEC conveniently and effectively, which is expected to guide the design and optimization of engineering practices. [ABSTRACT FROM AUTHOR]

• Energy, exergy, environment analyses and multi-objective optimization are performed for vortex tube coupled with transcritical refrigeration cycle (TVTC). • The parametric investigation is done to assess the thermodynamic performance of TVTC. • The cooling capacity of TVTC is found to be 10.1 % to 21.1 % higher than that of TVCR. • The maximum COP and exergetic efficiency of TVTC are higher than those of TVCR. • The evaporator temperature is the most influential input parameter in multi-objective optimization. The present study deals with the thermodynamic investigation of vortex tube coupled with trans-critical vapour compression refrigeration cycle (TVTC), followed by environmental analysis and multi-objective optimization. In this research, effect of various operating and design parameters is studied on the performance of TVTC. Furthermore, a comparison is made between the outcomes of TVTC and simple trans-critical vapour compression refrigeration cycle (TVCR). Results show that the optimum gascooler pressure for TVTC is observed to be lower than that of TVCR. Also, the cooling capacity and COP of TVTC are observed to be 10.1 % to 21.1 % and 2.3 % to 11.3 %, respectively, greater than those of TVCR. Moreover, the exergetic efficiency of TVTC is 2.3 % to 11.3 % higher than that of TVCR for the investigated range of evaporator and gascooler exit temperatures. The environmental penalty cost (per unit cooling capacity) of TVTC is 3.5 % to 12.2 % lower than that of TVCR. Furthermore, the coefficient of structural bond is calculated in order to choose the most sensitive parameters for system's performance. Additionally, genetic algorithm-based multi-objective optimization has been performed, with the evaporator temperature serving as the primary determining factor in establishing the optimal solution. This finding can guide the development of TVTC-based systems for a wide range of applications. [ABSTRACT FROM AUTHOR]

The task scheduler belongs to the NP-complete problem, so it is very challenging in the cloud environment to develop one with reasonable performance and computation speed. Several studies take into account some important factors of the users or providers when addressing cloud task scheduling. This paper models cloud task scheduling as a Multi-objective Optimization Problem (MOP) that maximizes load balancing and execution times. Based on ring topology, we present a new multi-objective particle swarm optimization approach that utilizes intuitionistic fuzzy set to enhance evenness and diversity. The diversity and spread of the Pareto solution is adjusted using a Balanced Intuitionistic Fuzzy Crowding-Distance (BIF-CD) and Intuitionistic Fuzzy non-dominance sorting (IF-dominance). In order to identify the best compromise solution and in decision space and adjust evenness in objective space, ring topology is employed to identify a larger number of Pareto-optimal solutions. Experimental results for 25 benchmark multi-objective functions demonstrate the superiority of the proposed IF-MO-Ring-PSO over four state-of-the-art algorithms. We compare different quantitative measures to assess the uniformity and quality of Pareto fronts (PFs) found by our compared methods. The performance of the proposed method is evaluated on the benchmark test suites ZDT, DTLZ, CF, mDTLZ, and MMF, using the delta and space metrics and the progression metrics. According to the proposed method, ZDT test suite reduced space and delta metric by 13.36 and 15.11% respectively compared to other methods. The Wilcoxon's test and two-sample Mann Whitney's test are used to analyze the performance of proposed method on CF test suit. The analysis shows that the proposed method has better ability to generate quality PF with uniformity on constraint test suits. In comparison to four scheduling algorithms, the proposed scheduler also shows better performance according to load balancing, makespan, and resource utilization based on two datasets (i.e., HCSP and GoCJ). [ABSTRACT FROM AUTHOR]

In the realm of heterogeneous computing, the efficient allocation of resources is pivotal for optimizing system performance. However, user-submitted tasks are often complex and have varied resource demands. Moreover, the dynamic nature of resource states in such platforms, coupled with variations in resource types and capabilities, results in significant intricacy of the system environment. To this end, we propose a scheduling algorithm based on hierarchical reinforcement learning, namely MD-HRL. Such an algorithm could simultaneously harmonize task completion time, device power consumption, and load balancing. It contains a high-level agent (H-Agent) for task selection and a low-level agent (L-Agent) for resource allocation. The H-Agent leverages multi-hop attention graph neural networks (MAGNA) and one-dimensional convolutional neural networks (1DCNN) to encode the information of tasks and resources. Kolmogorov–Arnold networks is then employed for integrating these representations while calculating subtask priority scores. The L-Agent exploits a double deep Q network to approximate the best strategy and objective function, thereby optimizing the task-to-resource mapping in a dynamic environment. Experimental results demonstrate that MD-HRL outperforms several state of the art baselines. It reduces makespan by 12.54%, improves load balancing by 5.83%, and lowers power consumption by 6.36% on average compared with the suboptimal method. [ABSTRACT FROM AUTHOR]

Food products are a critical part of everyday life. To increase the efficiency of the food supply chain, designing a comprehensive mathematical is necessary. This study tries to optimize a protein supply chain. This supply chain is divided into livestock and perishable products. The integration of these two supply chain echelons has been applied to create an extensive model. Moreover, sustainability has been considered as a competitive advantage in the chain. Perishable products are temperature-sensitive. Hence, a cold supply chain has been considered. The model has three objective functions: maximizing the total profit, minimizing the storage cost in the cold chain, minimizing the health risk. In dealing with uncertainty, a data-driven robust optimization method has been used. Therefore, this paper used machine learning to construct the uncertainty sets from historical data. The Torabi-Hassini method has been implemented to solve the multi-objective model. Finally, to show the applicability and efficiency of the proposed approach, a real-world case study on the poultry supply chain, including abattoirs, breeding centers, slaughtering, and selling branches, has been applied. The result shows that this methodology significantly influences total profits and improves the environmental criteria in a real-world case study. Moreover, different sensitivity analyses have been prepared to help managers make a trade-off between the robustness of the model and objective function value with various weights and calculate the influence of supply chain integration on objective functions. Highlights: A novel comprehensive mathematical model is developed to integrate livestock and perishable products in a sustainable supply chain. Cold supply chain management has been applied for fresh and frozen products. A machine learning mechanism has been used to deal with uncertain parameters. Sensitivity analysis and managerial tips are prepared according to real-world case study. [ABSTRACT FROM AUTHOR]

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions. • Presenting a multi-objective model for energy management via day-ahead scheduling in smart grids. • Enhancing day-ahead scheduling with renewables and demand response strategies. • Introducing a probabilistic model for solar and wind energy uncertainty prediction. • Proposing a hybrid demand response to lower peak energy demand and prevent rebound peaks. • Utilizing MOWDO algorithm for optimal Pareto fronts exploration to achieve tri-objective optimization: cost, emissions, and user comfort. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model's accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models. [ABSTRACT FROM AUTHOR]

As one of the important components of a ship, the ship's integrated power system is an important safeguard for ships. In order to improve the service life of the ship's power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship's integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship's ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship. [ABSTRACT FROM AUTHOR]

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field. [ABSTRACT FROM AUTHOR]

In eccentrically braced frames (EBFs), inelastic behavior is only permitted in the links. All members, except for the links, are designed according to the capacity design concept by using the link overstrength factor, Ω, so that they remain elastic even when the links develop their ultimate strength (including the strain-hardening effect). AISC 341 specifies that the Ω factor of link members must be 1.25 for beam and brace design and 1.1 for column design. In this study, the relevance of the current Ω factor was investigated. A total of 471 K-braced EBF systems with various conditions were designed using a multi-objective optimization technique, and nonlinear dynamic analyses were performed to evaluate the Ω factor. The results indicate that it is reasonable to use the current Ω factor for the design of beam outside link and brace; however, it leads to an overestimation of axial force in columns, especially in the lower stories of tall buildings. From the analysis results, a new Ω factor equation for column design was proposed. It was demonstrated that the structural quantities of 15-story frames designed using the proposed equation decreased by an average of 19% compared to those designed using the current Ω factor. [ABSTRACT FROM AUTHOR]

In order to achieve multi-objective optimization for a permanent magnet water pump motor in heavy commercial vehicles, we propose a strategy based on response-surface methodology and the improved sparrow algorithm (CGE-SSA). Firstly, the output capacity of the pump during actual operation was tested with an experimental bench to determine the design parameters of the motor, and then its modeling was completed using Ansys Maxwell 2022r2 software. Secondly, the response-surface model was established by taking the parameters of permanent magnet width, rib width, and slot width as optimization parameters and the output torque (Ta), torque ripple (Tr), and back electromotive force (EMF) amplitude as optimization objectives. Meanwhile, three methods—namely, circular sinusoidal chaotic mapping, improved golden sinusoidal strategy, and adaptive weight coefficients—were used to improve the convergence speed and accuracy of the sparrow search algorithm (SSA). Finally, the multi-objective optimization of the permanent magnet synchronous motor was completed using the improved sparrow algorithm. A comparative analysis of the motor's output before and after optimization showed that the torque pulsation and reverse electromotive force of the motor were significantly improved after optimization. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability. [ABSTRACT FROM AUTHOR]