NSGA-II Optimization Algorithm Research Articles

FLC-Based Ultra-Low-Frequency Oscillation Suppression Scheme for Interconnected Power Grids

The appearance of ultra-low-frequency oscillations in the grid at the sending end, after asynchronous grid interconnection, poses a significant threat to the stable operation of the system. For post-asynchronous interconnection in a multi-DC transmission system, an investigation is conducted to analyze the causes of ultra-low-frequency oscillations and the utilization of a Frequency Limit Controller (FLC) which aims to suppress these oscillations. Furthermore, a method is developed to rank DC sensitivity, considering the hydroelectric distribution in the sending-end grid, by combining the DC FLC impact factor and DC control sensitivity. Subsequently, a novel approach for ultra-low-frequency oscillation suppression is proposed. This approach employs the stochastic subspace method for parameter estimation and the NSGA-II optimization algorithm to convert the multi-DC optimization challenge into multiple sequential cyclic optimization problems, each focusing on a single DC, ensuring a more effective suppression of ultra-low-frequency oscillations. The proposed scheme’s effectiveness is validated through simulations using a specific locations’ interconnected power grid.

Research on thermal energy management for PHEV based on NSGA-II optimization algorithm

The plug-in hybrid electric vehicle (PHEV) is a new type of energy vehicle that has extremely high demands for vehicle energy consumption management. This study aims to achieve refined energy management of PHEVs, while avoiding high-energy consumption associated with independent control of the thermal management system. For this purpose, a reliable co-simulation platform based on a particular PHEV energy and thermal management system architecture is developed. The platform aims to optimize engine fuel consumption and ensure the stability of the coolant temperature. This study introduces the NSGA-II genetic algorithm to the field of vehicle R&D to collaboratively control the rotary speed of the pump and fan, which highly accelerating the vehicle development cycle. The results show that the optimized algorithm for engine thermal management system efficiently improved fuel economy under dynamics driving conditions. Under SFTP-US06 conditions, the fuel consumption of the PHEV reduced by 0.46L/100 km, resulting in a fuel-saving rate of 5.67%, and the average power of low-voltage accessories was reduced by 81.81%.The approach used in this research can increase the accuracy of the virtual calibration and help shorten the automotive development cycle.

Research on Multi-objective Optimization Model of Physical Education Teaching Enhancement Path in Colleges and Universities

Abstract This study begins by assessing the current state of physical education (PE) teaching in higher education institutions. It establishes an optimization index system for PE teaching, utilizing the entropy weighting method to calculate the weights of the indices. These weights are then incorporated into the objective function to define the constraints for optimizing PE teaching, facilitating the construction of a multi-objective optimization model tailored to enhance PE instruction pathways. The model is solved using the NSGA-II optimization algorithm, and an illustrative analysis is conducted. The results indicate a smooth distribution of the superiority and inferiority degrees within the derived PE teaching optimization scheme, characterized by low overall dispersion (mean superiority and inferiority value of 74.2841) and minimal deviation compared to the current status (mean value of 0.09835). This confirms the effectiveness of the proposed model in improving PE teaching. The implications of this study are significant for elevating the standard and quality of PE teaching in colleges and universities, nurturing more professionals for China’s sports industry, and advancing the nation’s sports sector to unprecedented levels.

Simulation and Experimental Study on the Precision Molding of Irregular Vehicle Glass Components.

The high level of stress and dimension deviation induced by glass molding are the main causes of the low yield rate of large, irregular glass components on vehicles. To solve this issue, a numerical model of large glass component molding was established in this study, which aimed to analyze the dominant factors of molding quality and achieve a synergistic balance between quality characteristics and energy consumption. The results show that molding temperature is the dominant factor affecting the energy consumption and residual stress, and the molding pressure is the main factor affecting the dimension deviation. Furthermore, the NSGA-II optimization algorithm was used to optimize the maximum residual stress, dimension deviation, and energy consumption with the numerical results. The combination of a heating rate of 1.95 °C/s, holding time of 158 s, molding temperature of 570 °C, molding pressure of 34 MPa, and cooling rate of 1.15 °C/s was determined to be the optimized scheme. The predictive error of the numerical result, based on the optimized scheme, was experimentally verified to be less than 20%. It proved the accuracy of the model in this study. These results can provide guidance for the subsequent precision molding of large, irregular glass components.

An Analysis Model for Predicting Windage Power Loss of Aviation Spiral Bevel Gears Under Optimal Injection Jet Layout

With the increasing rotating speed of aviation spiral bevel gears, the load-independent windage power loss caused by hydrodynamic behavior has a greater impact on transmission efficiency. An analytical model is established to predict the windage power loss of spiral bevel gears with oil injection lubrication. The improved model regards the total windage losses as the sum of oil–gas dragging effects on the tooth flank, toe/heel, circular cone surface, circumference of the teeth, and tooth root. Then the numerical method and analytical method are combined to calculate windage losses. A multi-objective NSGA-II optimization algorithm is used to optimize the jet layout parameters. The rationality and accuracy of this model are verified by comparing the calculation results of an example with data sets of existing experimental findings and computational fluid dynamics (CFD) simulation results. The calculation results show that compared with the tooth profile assumption method in the existing literature, the improved calculation formula of windage dragging effect on tooth flank can better reflect the influence of oil injection lubrication layout parameters on windage loss and make the windage loss calculation more accurate. Finally, the influence of gear body parameters, working conditions parameters, geometric parameters, and injection lubrication layout parameters on the windage loss is examined. This research provides a theoretical basis and methodological guidance for optimization design of reducing windage power loss.

Research on safe charging strategy of lithium-ion battery based on three-electrode equivalent circuit model

Under extreme conditions of low temperature or fast charging, the side reaction of lithium (Li) plating can occur on the anode surface of lithium-ion batteries (LIBs), which may cause causing capacity fade. Alarmingly, in severe cases, dendrites will grow and pierce the separator. As a result, thermal runaway will be induced, which seriously affects the safety performance of LIBs. Along these lines, to avoid Li plating during the charging process, the safe charging strategy was thoroughly investigated in this work based on the implementation of the three-electrode equivalent circuit model (ECM). More specifically, for the three-electrode LIB implanted with a reference electrode (RE), its ECM was improved based on the traditional ECM. The cathode and anode of the LIB were decoupled through the RE, whereas combined with the heat generation, transfer, and aging characteristics, a first-order RC electro-thermal coupled model of three-electrode was established, and the semi-empirical aging model of the LIB was combined to analyse the coupling characteristics between the parameters of each model. According to the acquired experimental data, the key parameters of modelling were identified and the accuracy of the model was verified. In addition, aiming at the contradiction between the fast charging and aging speed, and combined with the NSGA-II optimization algorithm, the self-adaptive multi-stage constant current constant voltage (SMCCCV) charging strategy was also proposed to avoid Li plating on the anode surface. An optimization model was thus established with the charging time and aging loss as the objective functions, resulting in three charging strategies, namely minimum charging time, minimum aging, and balanced charging. From the simulated outcomes, it was demonstrated that the anode potential was strictly maintained above the safety threshold for these three strategies. On top of that, the balanced charging strategy reduced the aging loss by 11.5 %, while the relatively equivalent charging speed compared with the traditional constant current constant voltage (CCCV) strategy was maintained. The proposed safe charging strategy based on multi-objective optimization provides some valuable insights for next-generation fast charging techniques.

Multi-objective optimization of campus microgrid system considering electric vehicle charging load integrated to power grid

The increasing use of renewable energy sources and electric vehicles (EVs) has necessitated changes in the design of microgrids. In order to improve the efficiency and stability of renewable energy sources and energy security in microgrids, this paper proposes an optimal campus microgrid design that includes EV charging load prediction and a constant power support strategy from the main grid. The problem of load variation due to changes in the number of EVs connected to the microgrid will occur, and this paper presents a detailed prediction method and effective solutions. The load profile of EVs connected to the microgrid is simulated using the Monte Carlo (MC) method, taking into account EV owners’ usage habits, including charging options and dwell time, as well as battery parameters, including state of charge (SOC) and size. The simulation results show that the peak-to-valley difference in grid power after adding EVs is close to 14 times due to the uniformity of travel between staff and students. Based on how long EVs stay in the parking place, a charging and discharging policy for participation in grid dispatch is developed, which has reduced the gap between the peak and the valley. This paper gives precedence here for the main grid to provide constant power support, which would limit the electricity consumption of the campus, reduce the dependence on the main grid and moreover increase the utilization of renewable energy by the microgrid. The ideal solution set for this microgrid system model’s best configuration is found using the NSGA-II optimization algorithm. To select the most suitable option, the TOPSIS method is employed. The simulation results show that the microgrid system can operate with EV integration in an economical and stable manner. Additionally, the peak-to-valley value and CO2 emissions are reasonably reduced, and the income of EV users is increased. At the same time, the microgrid operator’s charging fee for EVs can lower operating costs and also suggests that future microgrid electricity sales will be more accessible and transparent.

Development of an integrated multi-objective approach to formulate optimal harvesting policies with the aim of sustainable management of groundwater resources: study area: Varamin Plain

Abstract In the present study, an integrated multi-objective simulation -optimization model was developed using a NSGA-II optimization algorithm and GMS simulation software with the aim of sustainable harvesting from the Varamin aquifer. For this purpose, the quantitative model of the aquifer was simulated. Based on the calibrated model and integrated with the optimization algorithm, a two-objective management model was developed to control the exponential drop in the groundwater level and ensure sustainable harvesting from the wells. Then, according to multi-criteria decision-making methods and the Borda aggregation method, the optimal harvesting scenario of wells in operation was analyzed. The results show that more than 69% of the wells in the study area have experienced a rise of up to 30 m in the groundwater level during a 1-year period. It indicates the relative improvement of the aquifer in the direction of restoring its saturated thickness. Also, the comparison of the results obtained from the optimal and existing conditions showed that by applying the proposed model, an average of 46.9% can be saved in the harvesting of groundwater resources. For this purpose, it is necessary to reduce the allowed amount of harvesting from wells in this area from 21 to 11 L/s.

Multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development

Cost-effective runoff control scheme drafting involves localization, multi-sector coordination, and configuration of multifunctional infrastructures. Numerous independent variables, parameters, weights, and objectives make runoff control optimization quantitatively arduous. This study innovatively proposed a multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development. The quantitative methods of multi-objective evaluation, hydrological feature partition, and pressure-adapted multi-objective weight assignment were proposed. Remote sensing inversion of water quality, hydrological model simulation (using SWAT and SWMM software), landscape pattern index calculation, life cycle cost (LCC), life cycle assessment (LCA) on ecological impact, and NSGA-II optimization algorithm were applied. Wuhan, the most water-sensitive city in China, was studied as a case. Runoff control function (RCF), capital investment (CI), and ecological return on investment (EROI) served as optimized objectives. High, medium, and low built-up regions in Wuhan urban development planning district were extracted by topographic factors and landscape patterns, which comprised 28, 34, and 38% of the case area, respectively. Three corresponding hydrological models were then built to illustrate distinct runoff control cost-efficiency in each region. Pressure distributions on runoff control, economic constraints, and ecological resource scarcity were quantitatively evaluated. And four pressure zones were clustered, which occupied 36, 29, 16, and 19% of the case area, respectively. Then the zonal weighted optimization decision-making matrix (with 3 hydrological models and 5 wt) was established by overlaying the pressure zone and built-up zone. In high, medium, and low built-up regions, optimized solutions reduced annual runoff volume by 86, 82%, and 77%The average runoff investments per square meter of impervious underlying surface in high, medium, and low built-up regions were 34.2, 18.7, and 7.9 RMB yuan, respectively. Medium and low built-up regions may only need 55 and 23% of the high built-up region for the unitary impervious underlying surface to balance runoff control and ecological benefits. Runoff control and financial utilization efficiency enhance with hydrological differentiation zones. Thus, the optimization solutions are zonal adaptive, refined, comparable, replicable, and implementable.

Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation

The rotary vector reducer presents high precision and load capacity characteristics. The shape of the cycloidal gear tooth profile in the rotary vector reducer significantly affects its performance. Meanwhile, the effect of the machining error on the error between the designed and theoretical tooth profiles cannot be ignored. Thus, this paper analyzes the machining error items that affect the shape of the cycloidal gear profile from the machining process perspective. Due to the random characteristics and different distribution rules inherent in various machining errors, this paper proposes a cycloidal gear machining error compensation and modification model based on the Monte Carlo simulation method, providing a new theoretical method for compensating cycloidal gear machining errors. While compensating for machining errors, considering the impact of cycloidal gear modification on the carrying capacity, the NSGA-Ⅱoptimization algorithm is utilized to optimize the cycloidal gear modification parameter and finally, to solve the modification parameters with a more comprehensive performance.

The Optimization of The Zinc Electroplating Bath Using Machine Learning And Genetic Algorithms (NSGA-II)

In this study, our aim is to predict the compositions of zinc electroplating bath using machine learning method and optimize the organic additives with NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization algorithm. Mask RCNN was utilized to classify the coated plates according to their appearance. The names of classes were defined as ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail”. The intersection over union (IoU) values of the Mask RCNN model were determined in the range of 93–97%. Machine learning algorithms, MLP, SVR, XGB, RF, were trained using the classification of the coated panels whose classes were detected by the Mask RCNN. In the machine learning training, the additives in the electrodeposition bath were specified as input and the classes of the coated panels as output. From the trained models, RF gave the highest F1 scores for all the classes. The F1 scores of RF model for ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail” are 0.95, 0.91, 1 and 0.80 respectively. Genetic algorithm (NSGA-II) was used to optimize the compositions of the bath. The trained RF models for all the classes were utilized as the objective function. The ranges of organic additives, which should be used for all the classes in the electrodeposition bath, were determined.

Optimum Power Flow with Respect to the Capacitor Location and Size in Distribution Network

In the past few decades, there has been increasing recognition of the importance of optimum power flow (OPF) studies in the context of economic analyses of power systems. There is a need for power system development to maximize efficiency by emphasizing cost and power losses for smart grids to operate effectively in the current situation. This study aims to develop an optimal capacitor bank allocation schedule that minimizes power losses in the distribution networks under equality constraints. This will be achieved by integrating the loss factor and voltage stability into a new approach to determine where the capacitor banks should be located. It aims to reduce the operating costs of power systems and maximize efficiency by applying an optimization model for economic dispatch, which considers distributed power generation and demand response. The NSGA-II optimization algorithm was used in this study to determine the optimal size and location of the capacitor bank. A NSGA-II solves this problem by minimizing cost and power losses while determining the best operating strategy. We used an IEEE 26-bus distribution system to test the proposed method with every possible generation change. Comparing the power flow analysis with/without capacitor optimization showed that the operation optimization model of OPF with NSGA-II can reduce operation costs and improve the power system.

Design, optimization and application of two-element airfoils for tactical UAV

Owning to the requirements for longer endurance, larger payloads as well as simpler mechanical structure of tactical UAV, the two-element airfoil technology is introduced. Firstly, the aerodynamic characteristics of five common low-speed airfoils are calculated and compared. The airfoil with good aerodynamic performance after segmentation is selected as initial airfoil. Then, the influence of different segmentation types on the aerodynamic characteristics of airfoils is analyzed, and the segmentation type which brings the best aerodynamic benefits is selected. The slot parameters are optimized by NSGA-II optimization algorithm under cruising, take-off, and a large angle of attack states to obtain a high-performance self-adaption tactical UAV airfoil. Finally, the new airfoil is applied to Sparrow-hawk tactical UAV, the CL1.5 /CD of acceleration state increases 15.5%, the CL of large angle of attack increases 20.4% and the CL1.5 /CD of cruising state after enlarge the aspect ratio increase12.3%.

Undeterministic analysis and process optimization for short-fiber composite injection molding

When designing vehicle parts, reliability is required to be insensitive to the change of design parameters, so as to improve product safety and robustness. The traditional design method mainly considers the deterministic model and ignores the influence of noise factors. Based on deterministic optimization, a multi-objective robust design model with multi-quality characteristics is proposed in this paper. The response surface model (RSM) of controllable factors, noise factors and quality objectives is established. Then based on the RSM model, it is applied to do robust optimization design with undeterministic analysis method, 6 Sigma optimization design and NSGA-II optimization algorithm. Taking the optimization design of the vehicle navigation fixed base as an example, the feasibility and effectiveness of the optimization method are verified by numerical simulation and actual injection molding experiment respectively.The results show that the 6 Sigma robust design achieves the minimum mean square error and improves the robustness of the optimized solution, compared to the deterministic optimization.

A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system

High-speed railway, the backbone for the multi-modal corridor of metropolitan areas, stimulates intercity commute and travel. With growing intercity railway demand, the risk of in-station congestion and passenger anxiety should be taken seriously after rail disruptions. This research defines an evacuation planning problem for stranded passengers along the interrupted line which is regarded to be in series and growing with time. The proposed multi-modal evacuation-based approach responds to the risk of in-station congestion and reflects the mitigation efficiency of investing in multi-modal schedules. The post-disruption transit system for evacuation is formulated by demand, operation, routing, and transfers, upon which the bi-objective bi-level optimization model for multi-modal scheduling and routing is based. A hybrid NSGA-II and ant colony optimization algorithm are proposed to solve the model. A case study applies three transit-based response strategies on a high-speed railway disruption on the Beijing-Hebei metropolitan corridor. Results show that proposed strategy provides a lower level of risk with acceptable cost and evacuation equilibrium points relative to other strategies, and differentiate transit passengers in groups resulting in less delay and left quantity. With the investment in capacity on high-speed modes, like helicopters and high-speed trains, the evacuation speed for both pick-up and travel is increased, and the evacuated quantity is improved.

Thermal-economy optimization for single/dual/triple-pressure HRSG of gas-steam combined cycle by multi-objective genetic algorithm

This investigation proposed the RBF-NSGA-Ⅱ optimization algorithm and explored the thermal economy of different bottom combined cycles under 16 design parameters. The maximum efficiency, minimum exergy loss and minimum unit investment of different gas–water system combined cycles under various design factors were compared with traditional optimization, NSGA-Ⅱ optimization and RBF-NSGA-Ⅱ optimization. The results indicated that the combined cycle efficiency optimized by NSGA-Ⅱ and RBF-NSGA-Ⅱ increased by 25.2%, the exergy loss was reduced by 32%, and the specific investment was reduced by 38% compared with the traditional optimization. This investigation showed that the convergence speed of the newly proposed RBF-NSGA-Ⅱ optimization algorithm is increased by three times compared to the NSGA-Ⅱ optimization algorithm. Furthermore, through single-parameter sensitivity analysis, the relationship between compressor outlet pressure, gas turbine inlet temperature, pinch point temperature difference, approach point temperature difference, waste heat boiler pressure and combined cycle efficiency, exergy loss, and specific investment costs were investigated. The paper provided a new way for combined cycle design and guided the operation of combined cycle power plants.

An investigation on the performance of different reliability criteria for design of water distribution networks

ABSTRACT Due to the high construction costs of a water distribution network, the least cost design with the most reliability is of great importance. In this paper, a survey was conducted on the performance of different reliability metrics including nodal pressure (PIP), pipe velocity (PIV), pressure difference (ΔH), combined index of pipe velocity and nodal pressure (PI) as well as the network resilience index (NRI), minimum surplus head (MSH) and entropy reliability index (ERI) using NSGA-II optimization algorithm and EPANET hydraulic solver. Reliability metrics were tested on three benchmark water distribution networks: Hanoi, Pescara and Modena and the results were analyzed in three scenarios of 5%, 10%, and 20% pipe failure. Experimental results showed that NRI generally outperforms other metrics and the PIP index is in the second score. These metrics increase the average of network pressure and thus provide higher reliability in the situation of unexpected events like pipe breaks.

Analysis and optimization of vehicle transient characteristics under sine-swept steering input with considering steering system stiffness and damping

This study focuses on the influence of steering system stiffness and damping on the vehicle transient characteristics at two different types of handling frequencies, and optimizes the vehicle transient characteristics with steering system stiffness and damping as optimization variables at both handling frequencies. The kinematic relationship between wheel angle and steering wheel angle is derived as well as a detailed analysis of the forces on the steering linkage. The differential equations of vehicle dynamics are developed to explain the influence of steering system stiffness and damping on the vehicle transient characteristics theoretically. The stiffness and damping, which have a significant influence on the vehicle transient characteristics, are selected by sensitivity analysis and used as variables for simulation verification. Simulation results show that the influence of steering system stiffness and damping on vehicle transient characteristics is different at high handling frequencies and low handling frequencies. The stiffness and damping are used as optimization variables and the objective evaluation indexes of vehicle transient characteristics at high handling frequencies and low handling frequencies are used as optimization targets for multi-objective optimization by the NSGA-II optimization algorithm. The optimization results show that: The yaw rate gain relative to steering wheel angle and the delay time of lateral acceleration relative to steering wheel angle are improved at both low and high frequencies.

Urban storm water drainage system optimization using a sustainability index and LID/BMPs

• A new comprehensive sustainability index for urban stormwater drainage systems was developed. • The developed sustainability index applied to the western part of Tehran stormwater drainage. Results showed that the study area suffers from significant performance degradation. • Two multi-objective optimization models, with the objectives of maximizing sustainability and minimizing costs, were introduced to rehabilitate the stormwater drainage system. • The effects of considering channels rehabilitation along with BMPs implication were studied. • Results showed that rehabilitating the stormwater networks by BMPs implication and rehabilitating the channels would result in more cost-effective solutions. Urban stormwater drainage systems (USDSs) play an essential role in protecting cities from excessive rainfalls and urban floods. To quantify the performance of the USDSs, a new sustainability index is proposed based on reliability, resiliency, and vulnerability indices. These performance indices evaluate different aspects of a USDS with a comprehensive view of the system's level of service. Using the proposed sustainability index, two USDS design/rehabilitation optimization models are introduced and compared to see which one offers a more sustainable solution and lower implementation cost. The first model ( Case I ) applies Best Management Practices (BMPs) to an existing USDS to improve sustainability with minimum costs. The second model rehabilitates the channel sizes along with using BMPs. A part of the Tehran Stormwater Drainage System (TSDS) is chosen as the study area to evaluate the proposed methodology. Optimization models are developed by linking the NSGA-II optimization algorithm with the EPA SWMM model. The best solutions are extracted from both cases’ Pareto fronts and compared using the Nash bargaining method. Results showed that designing/rehabilitating USDSs by optimizing the BMPs size and channel size would result in a higher sustainability index and lower costs.

Study of Suspension Parameters Matching to Enhance Vehicle Ride Comfort on Bump Road

In this study, the parameters of the MacPherson front suspension and the E-type multilink rear suspension are matched to enhance the vehicle ride comfort on bump road. Vehicle vibration and suspension stiffness are analyzed theoretically. In the simulation study, the influence of the front and rear wheels on the vehicle vibration is considered, so the time-domain curves of the front and rear seat rail accelerations are processed by adding windows with two different window functions. The resulting ΔRmsLocal and ΔRmsGlobal are used as evaluation indexes of the vehicle ride comfort. The sensitivity analysis yields the magnitude of the influence of the suspension parameters on the evaluation indexes. In addition, the trends of ΔRmsLocal and ΔRmsGlobal with bushing stiffness at different vehicle speeds are discussed. The results show that longitudinal ΔRmsLocal and ΔRmsGlobal of the seat rails are influenced by the bushings mostly, while the vertical ΔRmsLocal and ΔRmsGlobal of the seat rails are influenced by the spring and shock absorber mostly. The trends of ΔRmsLocal and ΔRmsGlobal with bushing stiffness are influenced by the speed of the vehicle. Finally, the vehicle ride comfort is enhanced after optimization and matching of the suspension parameters by NSGA-II optimization algorithm.