NSGA-II Algorithm Research Articles

Scheduling of Collaborative Vegetable Harvesters and Harvest-Aid Vehicles on Farms

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.

Expanding the horizons of metal additive manufacturing: A comprehensive multi-objective optimization model incorporating sustainability for SMEs

Metal Additive Manufacturing (MAM) has seen significant growth in recent years, with sub-processes like Metal Material Extrusion (MEX) reaching industrial readiness. MEX, known for its cost-effectiveness and ease of integration, targets a distinct market segment compared to established high-end MAM processes. However, despite technological improvements, its overall integration into the industry as a viable manufacturing technology remains incomplete. This paper investigates the competitiveness of MEX, specifically its integration into the supply chain and the implications on cost and carbon emissions. Utilizing real-world data, the research develops a multi-objective optimization (MOO) model for a four-echelon supply chain including suppliers, airports, production facilities, and customers. The optimization model is combined with a previously developed cost model for MEX to optimize facility location in Norway using the NSGA-II algorithm. Employing a case study approach, the paper examines the production of an industrial part using stainless steel 17-4PH, detailing concrete process costs and system-level costs across four different production scenarios: 10, 100, 1,000, and 10,000 parts. The findings indicate MEX’s potential for cost-effective production at low and diversified volumes, supporting the trend towards customization and manufacturing flexibility. However, the study also identifies significant challenges in maintaining competitiveness at higher production volumes. These challenges underline the necessity for further advancements in MEX technology and process optimization to enhance its applicability and efficiency in larger-scale production settings.

Design of Vibration and Noise Reduction for Ultra-Thin Cemented Carbide Circular Saw Blades in Woodworking Based on Multi-Objective Optimization

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.

Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.

Online ultrasonic monitoring of FDM nozzle temperature based on metamaterial buffer rod

Abstract Fused deposition modeling (FDM) has been widely used for fabricating parts with complex geometries. However, during the printing process the nozzle temperature fluctuates which may cause nozzle blockage, inter-filament delamination, over-melting, etc. Therefore, it is of significance to develop an online and in-situ nozzle monitoring system which could overcome such drawbacks for quality assurance. In this work, a multifunctional system is designed by incorporating a traditional ultrasonic probe and a metamaterial buffer rod to online monitor the nozzle temperature and ensure a correct extrusion force and speed. A parametric simulation model is first constructed implicitly based on thermal-acoustic metamaterial structures for the buffer rod. Secondly, since the high-fidelity calculation is computationally expensive, an efficient surrogate model with kriging approach is constructed based on the dataset drawn from limited trials for rapid prediction. Thirdly, multidisciplinary optimization is performed by the NSGA-III algorithm considering the thermal insulation performance, ultrasonic attenuation and structure integrity. The rod is fabricated according to the best parameters decided. A cross-correlation algorithm is calibrated with thermocouple measurement after waveform recording at different temperatures. The online and in-situ temperature measurement can be realized during 3D printing successfully eventually. This will help to improve the printing quality with potential feedback strategies.

Research on optimization of the vehicle handling stability considering flexibility of the front subframe

The elastic deformation of the front subframe during vehicle traveling affects the vehicle handling stability. The static equilibrium equations of the suspension and subframe system and the kinematics equations of the three-degree-of-freedom vehicle are established to illustrate the principle of the influence of the flexible front subframe on the handling stability. The front subframe is flexibly processed to establish a rigid-flexible coupled vehicle model. Simulations of the suspension kinematic and compliance alongside sine swept steering simulation of the vehicle are performed. The front subframe bushing stiffness, which has a greater impact on the handling stability, is selected as a design variable through parametric test design, and the NSGA-II algorithm is used for multi-objective optimization with the vehicle handling stability evaluation index as the optimization target. The simulation results show that the flexible front subframe will make the bushing force change to affect the suspension kinematic and compliance characteristics and then affect the vehicle handling stability, the gain of vehicle yaw rate and the delay time of lateral acceleration decrease, while the gain of roll angle increases. The optimization results show that the optimized vehicle exhibits improvements in the yaw rate gain, delay time of lateral acceleration, and roll angle gain.

Multi-objective optimization design for anti-fatigue lightweight of dump truck carriage combined with machine learning

As urbanization continues to accelerate, dump trucks assume an increasingly important role in the transportation and construction of infrastructure. The carriage represents a critical structural assembly of dump trucks. One of the primary failure modes of the carriage is weld fatigue failure, which frequently gives rise to the problem of weld fatigue cracking during transportation. To increase the fatigue life of welds and enhance the degree of structural lightweight of a heavy dump truck carriage, a method for anti-fatigue lightweight design based on machine learning and multi-objective optimization is proposed. A high-fidelity finite element model of the carriage is established for static simulation analysis of the typical conditions. Based on the virtual reliability simulation test of the dump truck and the equivalent structural stress method, the fatigue life of the critical welds in the carriage is calculated. The important part thicknesses are selected as design variables through the comprehensive contribution analysis method. The maximum displacement and maximum stress under the dangerous condition are considered as constraints. The mass of the carriage and the minimum fatigue life of the critical welds are considered as optimization objectives. The GA-XGBoost machine learning approximation models (GA-XGBoost-MLAM) and NSGA-II algorithm are employed for multi-objective optimization design of the carriage. The entropy weighted TOPSIS method is utilized for multi-objective decision-making of Pareto solutions. The design after optimization and decision-making shows that, while satisfying the requirements of static structural performance, the minimum fatigue life mileage of the critical welds of the carriage is increased by 157,570 km, representing an increase of 36.58%. Additionally, the mass of the carriage is reduced by 295.69 kg, representing a decrease of 9.47%. Therefore, the proposed design method achieves a good effect in the anti-fatigue lightweight of dump truck carriage.

Reactive power and voltage control based on reactive power compensation coefficient and sensitivity factor

Abstract Aiming at the problems that the existing reactive power allocation methods do not take into account the specific operating state of each doubly-fed induction generator (DFIG) and the established optimization model has many variables and is complicated, this paper proposes to construct an optimization model based on reactive power compensation coefficient and sensitivity factor, to reduce the number of variables in the mathematical model and simplify the expression of the relationship between each key parameter, so the speed of the reactive power and voltage control are improved. In the process of constructing specific optimization models, the objective of minimizing the active loss takes into account the operating copper consumption of each DFIG. The objective of minimizing the voltage fluctuation of each DFIG is also taken into account, in addition to the operating state constraints of each DFIG, so that the final reactive power allocation is more practical. In solving the multi-objective optimization problem, the improved NSGA-II algorithm is utilized, which can achieve the simultaneous optimization of multiple objectives, and when combined with the simplified model established in this paper, the correctness and speed of obtaining the optimal solution set of Pareto can be further improved. Finally, the effectiveness of the proposed strategy in reducing active losses and voltage fluctuations in wind farms is verified by simulation examples.

An efficient fake account identification in social media networks: Facebook and Instagram using NSGA-II algorithm

An efficient fake account identification in social media networks: Facebook and Instagram using NSGA-II algorithm

Process Parameters Optimization and Numerical Simulation of AlCoCrFeNi High-Entropy Alloy Coating via Laser Cladding.

The use of laser cladding technology to prepare coatings of AlCoCrFeNi high-entropy alloy holds enormous potential for application. However, the cladding quality will have a considerable effect on the properties of the coatings. In this study, considering the complex coupling relationship between cladding quality and the process parameters, an orthogonal experimental design was employed, with laser power, scanning speed, and powder feed rate as correlation factor variables, and microhardness, dilution rate, and aspect ratio as characteristic variables. The experimental data underwent gray correlation analysis to determine the effect of various process parameters on the quality of cladding. Then, the NSGA-II algorithm was used to establish a multi-objective optimization model of process parameters. Finally, the ANSYS Workbench simulation model was employed to conduct numerical simulations on a group of optimized process parameters and analyze the change rule of the temperature field. The results demonstrate that the laser cladding coating of AlCoCrFeNi high-entropy alloy with the single pass is of high quality within the determined orthogonal experimental parameters. The powder feed rate exerts the most significant influence on microhardness, while laser power has the greatest impact on dilution rate, and scanning speed predominantly affects aspect ratio. The designed third-order polynomial nonlinear regression model exhibits a high fitting accuracy, and the NSGA-II algorithm can be used for multi-objective optimization to obtain the Pareto front solution set. The numerical simulation results demonstrate that the temperature field of AlCoCrFeNi high-entropy alloy laser cladding exhibits a "comet tail" phenomenon, where the highest temperature of the molten pool is close to 3000 °C. The temperature variations in the molten pool align with the features of laser cladding technology. This study lays the groundwork for the widespread application of laser cladding AlCoCrFeNi high-entropy alloy in surface engineering, additive manufacturing, and remanufacturing. Researchers and engineering practitioners can utilize the findings from this research to judiciously manage processing parameters based on the results of gray correlation analysis. Furthermore, the outcomes of multi-objective optimization can assist in the selection of appropriate process parameters aligned with specific application requirements. Additionally, the methodological approach adopted in this study offers valuable insights applicable to the exploration of various materials and diverse additive manufacturing techniques.

Multi-objective Optimization-Based Algorithm for Selecting the Optimal Path of Rural Multi-temperature Zone Cold Chain Dynamic Logistics Intermodal Transportation

The road network in rural areas is complex and the infrastructure is relatively backward. The multi-temperature zone cold chain logistics involves agricultural products with different temperature requirements, which requires considering the transportation cost, carbon emission cost, refrigeration cost, and time cost of different temperature zones during path planning, thereby increasing the difficulty of path planning. Therefore, a multi-objective optimization-based algorithm for selecting the optimal path of rural multi-temperature zone cold chain dynamic logistics intermodal transportation is proposed. Based on the analysis of the multi-temperature cold chain collection and distribution model based on multimodal transportation, a multi-objective optimization model is constructed. This model aims to minimize transportation costs, carbon emission costs, refrigeration costs, time costs, and maximize logistics quality, while satisfying constraints such as transfer schedule times, the number of transport mode conversions, transport mode selection, and time continuity. To solve this model, an improved NSGA-II algorithm is adopted, which combines an improved mutation operator, congestion distance calculation, and the C-W saving algorithm to achieve the optimal transport path solution. Additionally, ArcGIS software is used to implement the shortest path planning based on real road networks. The experimental results show that by selecting the road-rail combined transport mode and adopting the D1–D6–D10 transport path, it is possible to transport fresh agricultural products from location A to the distribution center at location B, with the lowest Pareto fitness value. Furthermore, the algorithm's effectiveness is further verified by completing the end-of-life fresh agricultural product distribution task with four multi-temperature refrigerated vehicles. The study also finds that extending or shortening the latest service time window for customers, although it leads to a decrease or increase in the optimal value of the algorithm's objective function, has little impact on the average distribution time and transport vehicles. These findings provide new theoretical and practical guidance for the path selection of multimodal transportation in multi-temperature cold chain logistics, with significant theoretical and application value.

Research on Multi-Objective Low-Carbon Flexible Job Shop Scheduling Based on Improved NSGA-II

To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.

An Optimization Method for Location-Routing of Cruise Ship Cabin Materials Considering Obstacle Blocking Effects

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress.

Food risk assessment based on NSGA-II algorithm: a case study of alcoholic beverages

Food risk assessment based on NSGA-II algorithm: a case study of alcoholic beverages

Multi-objective optimization and long-time simulation of a multi-borehole ground heat exchanger system

Multi-objective optimization and CFD simulation are conducted to optimize the design of a multi-borehole ground heat exchanger (GHE) system and assess its long-time performance. The multi-objective optimization is performed to minimize the entropy generation number (EGN) and total cost rate by using various evolutionary algorithms, including NSGA-II, GDE-3, MOEA/D, PESA-II, SPEA-II, and SMPSO. NSGA-II and GDE-3 algorithms perform best in obtaining Pareto optimal solutions. Three prominent points on the NSGA-II Pareto frontier, representing the results of single-objective thermodynamic, single-objective economic, and multi-objective optimizations, are simulated in three dimensions over three months. The trends of EGN variations extracted from the transient CFD simulation agree well with those from the steady analytical model. The EGN obtained from multi-objective optimization is 58.8% lower than the EGN obtained using single-objective economic optimization and 1.9 times higher than that calculated from single-objective thermodynamic optimization. Likewise, the total cost rate obtained from multi-objective optimization is 64.4% lower than the value obtained from single-objective thermodynamic optimization and four times higher than that calculated using single-objective economic optimization. The proposed optimization approach can be reliably applied to improve the design of multi-borehole GHE systems.

Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability

Electrified transportation requires batteries with high energy density and high-rate capability for both charging and discharging. Li-ion batteries (LiBs) face a dilemma: increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer path. Tailoring cell design parameters to balance these metrics is essential but challenging. Here, we present a multi-objective optimization framework targeting energy density, fast charging, high-rate discharging, and lifespan simultaneously. Four cell parameters—cathode areal capacity, N-P ratio, cathode porosity, and anode porosity—along with operating temperature, are selected as design variables. A physics-based pseudo-2D model, validated against experimental data, generates data to train the surrogate model, which is combined with the NSGA-II algorithm for rapid optimization. Three different objective calculation methods are compared to identify the maximum sum of energy densities, lowest polarization, and most balanced performance, respectively. Cell design parameters are optimized at different temperatures using the most balanced optimization method. Results demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density, mitigating the energy-power trade-off and broadening battery design parameter ranges.

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Optimized scheduling of integrated community energy stations based on improved NSGA-III algorithm

To cope with the problem of building and using a large number of fast electric vehicle stations (FEVS), adding FEVS to the integrated community energy system (ICES) provides new ideas for solving the problems of interaction with the grid, overall system stability, revenue benefits, and environmental pollution. To this end, this paper designs an ICES model with FEVS (ICES-FEVS). This model considers economy, stability, and sustainability to achieve all-around consideration. To further solve the complex multi-objective problem, the multi-objective improvement strategy NSGA-III algorithm (IS-NSGA-III), combined with fuzzy comprehensive evaluation (FCE), is used to solve the model. In the solution phase, the advantages of the IS-NSGA-III algorithm are first verified by the benchmark function, and the validity of the ICES-FEVS model is verified by comparing different models. In the optimization results, daily revenue was increased by 6.3 %, daily battery life loss was increased by 26.03 %, and pollution emissions were reduced by 46.9 %. The analysis of the results shows that introducing Electric Vehicle (EV) fast charging stations in an integrated community energy system ensures economic improvement, sustainable development, and more favorable regulation of the energy storage system. The simulation results show that compared with other algorithms, the HV metrics of IS-NSGA-III are lower by about 6.8 %, Spread metrics are better by about 1.35 %, and the proposed IS-NSGA-III is better in terms of convergence and distributivity.

A novel multi-objective generative design approach for sustainable building using multi-task learning (ANN) integration

Building Performance Optimization (BPO) plays a pivotal role in enhancing building performance, guaranteeing comfort while reducing resource consumption. Existing performance-driven generative design is computational demanding and difficult to be generalized to other similar buildings with difficult to be generalized to other building types or climate conditions. To fill this gap, this paper introduces a novel framework, which integrates multitask learning (MTL), code compliance check, and multi-objective optimization through NSGA-III algorithm. This framework is able to identify Paratoo Optimal design solutions, which comply with building codes, at low computation costs. The framework begins with selecting key design variables that are critical to building energy, comfort performance and life cycle cost. It then employs MTL to enhance the model's accuracy while reducing computational costs. Next, we designed a code compliance checking module followed by the NSGA-III optimization process, with the objective of identifying solutions that comply with existing building codes. The results indicate that the proposed MTL network achieved an R2 score of 0.983–0.993 on the test set. In the particular case study where equal weights are preferred, this approach yielded noteworthy reductions of 27.65%, 19.55%, and 31.13% in Building Energy Consumption (BEC), Life Cycle Cost (LCC), and Residue of continuous Daylight Autonomy (RcDA), respectively, for a rural dwelling, and exclude solutions that fail to satisfy regulatory standards. This framework allows designer to input the weights of each objective based on their preference and can be applied to other building types and climate regions. Last, we develop a solution selection tool based on the results output by the framework we proposed, which can be found at https://github.com/LiMingchen159/Village-House-Design-Strategy-in-Hebei-Province-China.

Resonant MEMS Accelerometer with Low Cross-Axis Sensitivity-Optimized Based on BP and NSGA-II Algorithms.

This article proposes a low cross-axis sensitivity resonant MEMS(Micro-Electro-Mechanical Systems) accelerometer that is optimized based on the BP and NSGA-II algorithms. When resonant accelerometers are used in seismic monitoring, automotive safety systems, and navigation applications, high immunity and low cross-axis sensitivity are required. To improve the high immunity of the accelerometer, a coupling structure is introduced. This structure effectively separates the symmetric and antisymmetric mode frequencies of the DETF resonator and prevents mode coupling. To obtain higher detection accuracy and low cross-axis sensitivity, a decoupling structure is introduced. To find the optimal dimensional parameters of the decoupled structure, the BP and NSGA-II algorithms are used to optimize the dimensional parameters of the decoupled structure. The optimized decoupled structure has an axial stiffness of 6032.21 N/m and a transverse stiffness of 6.29 N/m. The finite element analysis results show that the sensitivity of the accelerometer is 59.1 Hz/g (Y-axis) and 59 Hz/g (X-axis). Cross-axis sensitivity is 0.508% (Y-axis) and 0.339% (X-axis), which is significantly lower than most resonant accelerometers. The coupling structure and optimization method proposed in this paper provide a new solution for designing resonant accelerometers with high interference immunity and low cross-axis sensitivity.