Non-dominated Sorting Genetic Algorithm II Research Articles

Linear Cutting Performance Tests and Parameter Optimization of Poplar Branches Based on RSM and NSGA-II

To reduce the cutting force and cutting energy consumption during the operation of tree-climbing pruning machines for poplar trees, a linear cutting test bench device for branches was designed according to the growth characteristics of poplar branches and pruning forestry requirements in this study. Firstly, the cutting mechanical analysis of poplar branches was carried out to explore the significance parameters affecting the cutting force, and then the cutting performance test and parameter optimization of poplar branches was carried out through the response surface method (RSM). The test results indicated that cutting speed, tool edge angle and tool back angle had significant effects on the ultimate shear stress, cutting power consumption per unit area, and the branch damage rate of poplar branches, and the established regression equation demonstrated high goodness of fit. Meanwhile, a second-order regression mathematical model was developed between ultimate shear stress, cutting the power consumption per unit area of the cut and the branch damage rate, and the significance parameter. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used for multi-objective optimization computation to obtain the optimal combination of cutting parameters as 3.02 m/s for cutting speed, 15° for tool edge angle, and 3° for tool back angle. In this case, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate of poplar branches were small, which were 346.63 kPa, 9.35 mJ/mm2, and 12.36%, respectively. Through the test verification, it can be seen that the relative error between the verification test and the predicted value of model was less than 7%. Moreover, under a cutting tool edge angle of 15°, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate were, respectively, reduced by 17.29%, 14.98%, and 34.21% compared with those under a cutting tool edge angle of 20°, which verifies the validity and reliability of the test results and the research method. This study can provide data support and reference for the research and development of energy-efficient poplar tree-climbing pruning equipment and related branch-cutting performance tests.

A new integrated remanufacturing process planning and scheduling model under uncertainties using extended non-dominated sorting genetic algorithm-II

Remanufacturing, with its environmental and economic implications, is gaining significant traction in the contemporary industry. Owing to the complementarity between remanufacturing process planning and scheduling in actual remanufacturing systems, the integrated remanufacturing process planning and scheduling (IRPPS) model provides researchers and practitioners with a favorable direction to improve the performance of remanufacturing systems. However, a comprehensive exploration of the IRPPS model under uncertainties has remained scant, largely attributable to the high complexity stemming from the intrinsic uncertainties of the remanufacturing environment. To address the above challenge, this study proposes a new IRPPS model that operates under such uncertainties. Specifically, the proposed model utilizes interval numbers to represent the uncertainty of processing time and develops a process planning approach that integrates various failure modes to effectively address the uncertain quality of defective parts during the remanufacturing process. To facilitate the resolution of the proposed model, this study proposes an extended non-dominated sorting genetic algorithm-II with a new multi-dimensional representation scheme, in which, a new self-adaptive strategy, multiple genetic operators, and a new local search strategy are integrated to improve the algorithmic performance. The simulation experiments results demonstrate the superiority of the proposed algorithm over three other baseline multi-objective evolutionary algorithms.

Adaptive Joint Multiobjective Operating Parameters’ Optimization for Active Direct Methanol Fuel Cells

The operating parameters of the active direct methanol fuel cell (DMFC) are essential factors affecting its power delivery performance. Different operating parameters lead to variations in the amount of methanol crossover in a DMFC, which might cause overpotential and cathode catalyst poisoning. Due to the complexity of the DMFC system, changes in operating conditions, and correlations among these parameters, it is challenging to maintain output power density while reducing the negative effects of methanol crossover. This paper proposes an adaptive joint optimization method for fuel cell operating parameters. The principle operating parameters are selected by the orthogonal tests, which include an adaptive numerical simulation and multiobjective optimization regarding cell output power density and methanol crossover. The selected parameter combinations are verified by an evaluation model that quantifies the influences of operating parameters on the active DMFC power density and its methanol crossover, where the nonlinear mapping function for the two optimization objectives is obtained. The nondominated sorting genetic algorithm‐II (NSGA‐II) is applied to rapidly obtain the optimal combination. The results show that with the optimal parameters, the maximum power density is increased by 16.7% and the methanol crossover is reduced by 35.1%.

Design and multi-objective optimization of combined air separation and ORC system for harnessing LNG cold energy considering variable regasification rates

Regasification of liquefied natural gas (LNG) releases significant cooling potential, but improper usage can easily lead to wastage of resources and environmental pollution. The study developed an integrated air separation system and Organic Rankine Cycle (ORC) power generation system to effectively harness the inherent cold energy within LNG. Two distinct optimization algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Particle Swarm Optimization (PSO), were employed to determine the optimal operational parameters. The results showed the superior performance of PSO over NSGA-II, as demonstrated by the exergy efficiency, net output power, and levelized cost of energy at 78.12 %, 2.29*1011 kW, and 0.0665 $/kWh, respectively. Additionally, an assessment of various factors influencing the system's performance was conducted. The performance of both conventional and proposed systems was studied under varying regasification rates. The findings indicated that incorporating the cold recovery and storage unit enhanced the efficient utilization of LNG cold energy, resulting in an increased valley-to-peak ratio of the total power output from 0.03 in the conventional system to 0.5. The proposed system exhibits alignment with the energy consumption demands of users, and this study is anticipated to furnish a theoretical foundation for harnessing the cooling capacity of liquid hydrogen.

NSGA-II Algorithm-Based Structural Parameters of Electric Pulse Rock-Breaking Electrode Bit Multi-Objective Optimization

Abstract High-voltage electric pulse (HVEP) electrode bit has a considerable influence on the drilling and rock-breaking (RB) efficiency. HVEP electrode bit was systematically studied to optimize the structural parameters in order to improve RB efficiency. This paper analyzed the impact of main structural parameters on electric field strength (EFS) and depth of penetration (DOP) during high-voltage electric pulse drilling. A structural optimization method integrating back propagation (BP) neural network and genetic algorithm for HVEP electrode bit was proposed. The method mapped the complex nonlinear relationships among electrode distance, electrode cone angle, electrode grounding span, etc., and EFS and DOP by establishing a BP neural network model, and adopted the non-dominated sorting genetic algorithm-II (NSGA-II) to optimize the main structural parameters. The simulation data showed that the combined BP neural network/non-dominated sorting genetic algorithm-II (BP-NSGA-II) was an effective tool for optimizing the injection molding process. The multi-objective optimization of the structural parameters of the HVEP electrode bit based on the NSGA-II algorithm was crucial to direct the choice of the process parameters of the HVEP electrode bit, boost the RB efficiency, and lower the energy loss during drilling.

Research on Multi-Objective Optimization of High-Speed Solenoid Valve Drive Strategies under the Synergistic Effect of Dynamic Response and Energy Loss

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.

Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach Exploration

Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria.

Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.

Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes

Accurate estimation of potential wildfire behavior characteristics (PWBC) can improve wildfire danger assessment. However, wildfire behavior has been estimated by most fire spread models with immeasurable uncertainties and difficulties in large-scale applications. In this study, a PWBC estimation model (named PWBC-QR-BiLSTM) was proposed by coupling the Bi-directional Long Short-Term Memory (BiLSTM) and quantile regression (QR) methods. Multi-source data, including fuel, weather, topography, infrastructure, and landscape variables, were input into the PWBC-QR-BiLSTM model to estimate the potential rate of spread (ROS) and fire radiative power (FRP) over western Sichuan of China, and then to estimate the probability density of ROS and FRP. Daily ROS and FRP were extracted from the Global Fire Atlas and the MOD14A1/MYD14A1 product. The optimal PWBC-QR-BiLSTM model was determined using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Results showed that the PWBC-QR-BiLSTM performed well in estimating potential ROS and FRP with high accuracy (ROS: R2 > 0.7 and MAPE<30%, FRP: R2 > 0.8 and MAPE<25%). The modal PWBC values extracted from the estimated probability density were closer to the observed values, which can be regarded as a good indicator for wildfire danger assessment. The variable importance analysis also verified that fuel and infrastructure variables played an important role in driving wildfire behavior. This study suggests the potential of utilizing artificial intelligence to estimate PWBC and its probability density to improve the guidance on wildfire management.

Multi-objective coordinated control strategy for mixed traffic with partially connected and automated vehicles in urban corridors

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC.

Multi-Objective Evolutionary Algorithm to Optimize IoT Based Scheduling Problem Using (NSGA-II Algorithm)

Due to the continual advancements in the Internet of Things (IoT), which generate enormous volumes of data, the cloud computing infrastructure recently has received the most significance. to meet the demands made by the network of IoT devices. It is anticipated that the planned Fog computing system would constitute the next development in cloud computing. The optimal distribution of computing capacity to reduce processing times and operating costs is one of the tasks that fog computing confronts. In the IoT, fog computing is a decentralized computing approach that moves data storage and processing closer to the network's edge. This research article discusses a unique technique for lowering operating expenses and improving work scheduling in a cloud-fog environment. Non-dominated sorting genetic algorithm II (NSGA-II) is a proposal that is presented in this paper. Its purpose is to allocate service requests with the multi-objective of minimising finishing time and running cost. Determining the Pareto front that is associated with a group of perfect solutions, which are sometimes referred to as non-dominated solutions or Pareto sets, is the fundamental objective of the Pareto NSGA-II. There is a contradiction between the environmental and economic performances, which is shown by the Pareto set of sub-optimal solutions that are the consequence of the bi-objective issue.

Design methodology based on multi-objective optimization with evolutionary algorithms for humidification-dehumidification desalination systems using updated approaches

This paper develops a methodology for multi-objective optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The methodology aims to address the problem of the close relation between variables based on a mathematical model that simulates design variants of humidification-dehumidification (HDH) desalination systems, where the variation of one parameter can affect another. The approach consists of an exhaustive study of the state of the art to incorporate as many results as possible, including variable and parameter constraints established by previous research. Energy efficiency and potable water production are optimized with respect to the total area of the system's active components and the ratio of seawater converted to potable water. The calculations generate 20 Pareto optimal solutions, with an average pure water production of 14.01 l/h, and an average of the total and solar heating areas of 22.43 m2 and 8.52 m2, respectively, including many other parameters with similar values compared to other publications. The methodology is flexible, updatable, and generates high efficiency values, confirmed by the comparison of equivalent parameters and the pinch analysis, also with respect to previous results. The sensitivity analysis performed, presents different trends in the relation between variables in this scenario contributing to feedback on the behavior of hyperparameters of the algorithm, indicating a very wide potential of solutions. For the water temperature entering the dehumidifier, the range between 30 and 37 °C is determined as the optimum range, however, this is not the only result from the trend analysis of these 20 solutions, compared with other references. This methodology is a contribution to the overall objective of improving the HDH technology design process; optimal solutions are obtained for systems of the configuration chosen to apply it, but it can be adapted to others.

Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Research on Optimized Allocation and Use Effect of Music Education Resources Based on MOOC Platform

Abstract The equitable distribution of educational resources epitomizes the essence of establishing a robust educational nation. Yet, the issue of unequal allocation of music education resources has largely been overlooked. This study addresses this gap by examining the current status of music resource distribution across colleges and universities in Province C. An objective function was formulated based on four key indicators reflective of music education resources. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective optimization algorithm, was employed to optimize this function and develop a novel resource allocation strategy. Upon implementation of this strategy, the optimized resource quantities at the sampled institutions were observed as follows: 1,131 square meters of space, 809 books, 72 musical instruments, and 7 personnel. Subsequent evaluation of the changes in students’ musical performance, pre-and post-implementation, revealed significant improvements. Specifically, the average music performance score in the experimental group increased by 7.32 points, compared to a 6.63-point increase in the control group, with both enhancements statistically significant as per the t-test results. Further, a survey assessing the perceptions of teachers and students towards the newly implemented program indicated that 66.8% of respondents endorsed the revised allocation. Collectively, these findings suggest that the optimized allocation strategy not only facilitated the music education resource distribution but also garnered a positive reception and improved student outcomes in music, thereby contributing constructively to the overarching goal of strengthening the educational infrastructure.

Design and analysis of solar hybrid combined cooling, heating and power system: A bi-level optimization model

The planning and operation optimization of hybrid combined cooling, heating and power (CCHP) systems is the prerequisite and foundation for its advantages such as economy, energy saving, and high efficiency. This study constructed a bi-level optimization model of a hybrid CCHP system. Firstly, an upper-level capacity planning model of the hybrid CCHP system is constructed considering system economy, energy, and environmental performance. Secondly, a lower-level operation optimization model is proposed, considering the operation and maintenance cost of the system. Thirdly, a multi-objective honey badger algorithm (MOHBA) is developed based on the characteristics of the studied problem and the features of the bi-level optimization model. Finally, the optimization results indicate that the hybrid CCHP can save 32.79 % of the total cost, 49.74 % of primary energy, and 60.18 % of carbon emissions compared to the separation production (SP) system. In addition, compared with improving the strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEAD), and multi-objective particle swarm optimization (MOPSO), the proposed MOHBA can provide superior optimization results. The proposed bi-level planning model achieves improved economic, energy, and environmental performance of the hybrid CCHP system compared to the single-level planning model for the given operation modes.

A Bi-Objective Traffic Signal Optimization Model for Mixed Traffic Concerning Pedestrian Delays

Urban traffic networks suffer in numerous ways from traffic congestion. Some of these adverse effects are increased travel times of cars, buses, bicycle users, pedestrians etc., with the addition of excessive greenhouse gas emissions. Transportation engineers and policy makers try to improve the quality of urban transportation systems by developing projects to enhance the pedestrian experience, reduce private car usage, reduce total time spent in the network through different control strategies, and diminish the detrimental effects. In this context, this study takes Connected and Automated Vehicles (CAVs) and pedestrians into account at signal-controlled intersections. A novel intersection signal control optimization methodology that incorporates pedestrian delays and vehicular emissions from CAVs is presented. Non-dominated sorting genetic algorithm-II is utilized to solve the multiobjective optimization problem. For the emission calculations, the MOVES3 emission model is utilized. The proposed framework is tested on real-world case study. Simulation experiments showed major improvements in pedestrian delays and lower emissions.

Aspiration level-based non-dominated sorting genetic algorithm-II and III for multi-objective shortest path problem in a trapezoidal environment

Aspiration level-based non-dominated sorting genetic algorithm-II and III for multi-objective shortest path problem in a trapezoidal environment

A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach.

Multi-Objective Finned-Tube Heat Exchanger Optimization Using a Genetic Algorithm

Heat exchangers are a significant component in many industries, particularly in energy conversion systems. The design of heat exchangers itself is a complex process because it involves experience-based decisions, numerous variables and parameters, and some of them are competing with each other. Genetic Algorithms (GAs) are one of the first evolutionary algorithms which remains one of the most extensively used non-linear optimization methods today. This study explores the implementation of Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for thermal design and optimization of a finned-tube heat exchanger. The chosen objective functions were minimizing the heat exchanger volume and minimizing the air side pressure drop. The decision variables for the design were tube outer diameter, number of tube rows, fin pitch, unit height, and unit width. The calculated parameters and estimated cost of both preliminary design and optimized design were also compared. The optimized design offered a bigger alternative design while meeting all the constraints according to standards and industrial needs. The optimization reduced annualized operational and maintenance costs by 228% and lowered air pressure drop by 413% with bigger heat exchanger volume of 12% compared to the preliminary design.

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator.

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) was improved by combining numerical simulations and experiments. A finite element model of the MPSA was developed; its maximum and mean relative absolute errors of electromagnetic force were 4.3% and 2.3%, respectively, under typical work conditions. Seven design parameters including the cone angle, cone length, depth of the inner hole of the coil skeleton, cone width of the armature, inner cone diameter, and initial position of the moving-iron core were selected for developing the model, and the coefficient of the variation in electromagnetic force, nominal acceleration, 95% of the maximum stable output electromagnetic force, and corresponding response time were used as the performance indicators. The constraint relation between each performance indicator and the influence of each design parameter on the performance indicators were revealed using the uniform Latin hypercube experiment design, correlation analysis, and the main effect analysis method. A multi-objective optimization mathematical model of the MPSA was developed by combining traditional surrogate and machine learning models. The Pareto solution set was obtained using the nondominated sorting genetic algorithm II (NSGA-II), and three decision schemes with different attitudes were determined using the Hurwicz multi-criteria decision-making method. The results showed that a strong contradiction exists among the 95% of the maximum stable output electromagnetic force and its corresponding response time and the coefficient of the variation in electromagnetic force. The cone angle considerably influenced the performance indicators. Compared with the initial design, the coefficient of the variation in electromagnetic force was reduced by 54.08% for the positive decision, the corresponding response time was shortened by 15.65% for the critical decision, and the corresponding acceleration was enhanced by 10.32% for the passive decision. Thus, the overall performance of the long-stroke MPSA effectively improved.