Non-dominated Sorting Genetic Algorithm II Research Articles

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

Multi-objective Optimization Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.

An Ecology-Oriented Single–Multi-Objective Optimal Operation Modeling and Decision-Making Method in the Case of the Ganjiang River

Hydro power has provided significant economic benefits to society due to its cleanliness and convenience. As the number of hydropower stations has increased, many serious ecological issues have also emerged. This study uses Wan’an Reservoir as its research object and investigates single–multi-objective optimal operation and decision-making regarding reservoirs for ecology-oriented operation, to meet ecological water demand and seek the optimal operation schemes for energy generation and ecological benefits. The full-process research is conducted based on the “objective-modeling constraint optimization scheme decision-making” framework. The Mann–Kendall test and ordered clustering method were used to diagnose the hydrological variation in the basin. Based on this, a hierarchical and phased ecological flow process was derived. The objectives were defined according to the flow process, and optimal operation models were constructed. The differential evolution algorithm (DE) and improved non-dominated sorting genetic algorithm-II (NSGA-II) were used to solve the models. A non-fitting curve method was used to determine the approximate inflection point of the Pareto front curve, and the curve was fitted linearly according to the approximate inflection point to obtain the conversion formula between the objectives. Based on the coefficient of variation and Mahalanobis distance, a new multi-attribute decision-making method for reservoir operation, CV-ITOPSIS, was constructed by improving the traditional TOPSIS. The results show that: (1) There is a piecewise linear contradiction between energy generation and ecological objectives, and the contradiction intensifies with an increase in incoming water frequency. (2) Before the approximate inflection point, the head significantly influences the conversion rate from the energy generation to ecology, while the discharge flow is the major influencing factor after the inflection point. The inflection point and the formula for the piecewise straight line can reveal the conversion law between the two objectives. (3) CV-ITOPSIS considers the degree of differentiation of index data and fully considers the correlation between indicators while retaining the good evaluation performance of the traditional method. It recommends the optimal benefit scheme for a multi-objective non-inferior solution set. The research results provide a theoretical foundation and decision support for the optimal ecological operation of the Ganjiang River Basin.

Optimization design and experimental research on cooling performance of U-shaped latticework cooling structure with perforations used for gas turbine blade

Latticework cooling structure provides great structural strength and well heat transfer enhancement commonly used for internal cooling of turbine blades. However, traditional rectangle latticework cooling structure raises friction loss badly and reduces cooling efficiency of turbine blades due to the increase of wetted surface area. A new latticework cooling structure with U-shaped sub-channel and perforations on raffles was proposed, aiming to promote heat transfer and reduce friction loss for latticework cooling structure. Numerical simulation, optimization method and experimental test are conducted to investigate heat transfer and fluid flow characteristics of this new latticework cooling structure. Optimization design based on response surface approximation model and non-dominated sorting genetic algorithms-Ⅱ was conducted and objectives were to maximize heat transfer and minimize friction loss. The optimal U-shaped latticework cooling structure with perforations was obtained and experimental research was conducted. Characteristics of heat transfer and friction loss between the optimal U-shaped latticework cooling structure with perforations and the rectangle latticework cooling structure were compared and analyzed. The experiment results showed that the overall heat transfer and thermal–hydraulic performances of the optimal U-shaped latticework cooling structure with perforations are 21.6–37.0% and 27.9–46.7% respectively higher than those of the rectangle latticework cooling structure while friction loss performance of the former is 16.3–29.2% lower than that of the latter as Reynolds number increases from 5000 to 50000. Moreover, empirical formulas for the optimal U-shaped latticework cooling structure with perforations and the rectangle latticework cooling structure are obtained. The present study indicates that the U-shaped latticework cooling structure with perforations not only maintains the superior heat transfer performance, but also reduces friction loss, which is mainly due to the perforation ribs reduce flow distance for part of coolant and also provide an impingement effect for the heated wall and enhance flow interactions and flow mixing.

Multiobjective route finding in a multimode transportation network by NSGA-II

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

Performance prediction of a ground source heat pump system using denoised long short-term memory neural network optimised by fast non-dominated sorting genetic algorithm-II

The ground source heat pump (GSHP) is a commonly employed technique that utilises geothermal resources to heat or cool buildings, offering an advantageous alternative for reducing conventional energy consumption. Considering that the output energy of GSHP system is a critical criterion for evaluating the geothermal energy extraction, this study proposes an improved long short-term memory (LSTM) model for predicting the energy output of geothermal heat exchangers (GHE) EGHE-PT and the electrical energy consumption EEC-PT of a GSHP system. The model is trained and validated using a comprehensive real-time monitoring dataset gathered over a four-year period from a three-story residential house situated in Cleveland, Ohio, USA. To process the raw data, the wavelet denoising method is employed, while the fast non-dominated sorting genetic algorithm-II (NSGA-II) is employed to automatically determine the optimal hyperparameters for the LSTM model. Comparative analyses with alternative prediction models demonstrate the superior performance of the Denoised-LSTM-NSGA-II model. The results indicate that the Denoised-LSTM-NSGA-II model yields reasonable prediction for the two performance indicators: EGHE-PT and EEC-PT (with the R2 of 0.91 and 0.89, respectively). Further examination reveals that outdoor temperature holds a significantly high importance rank within the Denoised-LSTM-NSGA-II model. This implies that the proposed method can achieve an acceptable level of accuracy by only utilising weather data from the preceding 5 days. The aforementioned findings present a potentially cost-effective approach for predicting the performance of GSHP system based on limited monitoring data.

Optimization of wind‐wave hybrid system based on wind‐wave coupling model

AbstractThe integration of wave energy converters (WECs) into floating offshore wind turbine (FOWT) can effectively reduce costs and increase power generation. When WECs are integrated into FOWT, the hydrodynamic interference, motion coupling, and other factors contribute to the high spatial dimension of the coupled optimization, making it difficult to find the globally optimal solution. Therefore, this study proposes an optimization method based on a wind‐wave coupling model, and takes a new wind‐wave hybrid system as an example for verification and analysis. First, the experimental design is completed through random sampling, and the corresponding WECs and wind turbine power of each sample point are calculated using full coupling simulation. And then according to the design input and simulation results, the wind‐wave coupling model is obtained by training the elliptical basis functions neural network (EBFNN). At last, based on this model, the non‐dominated sorting genetic algorithm II (NSGA‐II) is used to optimize the WECs microarray. The results show that the prediction model established in this paper has high accuracy and is used with the NSGA‐II to effectively improve the wind‐wave coupling energy harvesting. This method can effectively solve the problem of high coupling dimensions in the process of hybrid system design.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Advanced design of cathode array protrusion structure of solid oxide fuel cell based on NSGA-II multi-objective optimization

As fuel cells gain widespread utilization, augmenting the power density and combination property of Solid Oxide Fuel Cells (SOFCs) has emerged as a pivotal area of research. This study introduces an innovative arrayed protrusion structure within the cathode flow channels of SOFCs to induce secondary flow. The influence of this arrayed structure on fuel cell combination property is meticulously analyzed through comprehensive numerical simulations. Furthermore, this paper employs the Response Surface Methodology to evaluate the synergistic effects of cathode operational conditions, particularly temperature, and arrayed structure parameters (including base width, height, and quantity) on SOFC performance. Correlations among these variables have been established to show synergistic associations between variables and performance index. The findings reveal that the inclusion of an arrayed structure in the cathode flow channels generates multi-directional vortices, thereby intensifying gas disturbance, facilitating reactant diffusion, and optimizing oxygen concentration distribution. This results in a marked enhancement of the fuel cell's overall performance. Among the operational parameters analyzed, temperature exerts the most substantial influence on power density, followed by protrusion height, base width, and number. Ultimately, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Pareto optimal solutions were derived, yielding a superior balance of higher power density and reduced pressure drop. Compared to the baseline, the power density in these Pareto optimal solutions saw an increase of 16.5% to 23.6%, while pressure drop escalated marginally by 1.5% to 8.9%.

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

A robust machine learning-based framework for handling time-consuming constraints for bi-objective optimization of nonlinear steel structures

In structural design, multi-objective optimization of nonlinear steel structures simultaneously considers various design (often conflicting) purposes during the stage of construction to the operation of projects. Such optimization generally requires excessive computational efforts for the evaluation of time-consuming constraints. To overcome this drawback, this paper presents an efficient machine learning-based (ML) method to perform bi-objective optimization of nonlinear steel structures describing two conflicting objectives, namely minimization of structural weight and nodal displacements, simultaneously. The proposed method combines a nondominated sorting genetic algorithm-II (NSGA2) with an extreme gradient boosting (XGBoost) surrogate-assisted model. Furthermore, a multi-objective comparison technique (MulCT) is implemented, where a safety parameter t is introduced to reduce errors in the predictive ML model. To demonstrate the efficiency of the proposed method, two robust design examples, including a planar 10-bar truss and 47-bar transmission tower structures, are provided. In comparison with various ML algorithms, numerical results illustrate that the XGBoost model is superior in performance and requires significantly less training time. The MulCT technique enhances computational efficiency, leading to a 20% reduction in computing resources as when it was implemented with NSGA2. The proposed method by integrating both MulCT and XGBoost models with NSGA2 can save more than 50% of computational efforts, while maintaining good accuracy, as can be achieved by NSGA2.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Multi-objective parameter optimization of the Z-type air-cooling system based on artificial neural network

The performance of electric vehicles is significantly influenced by the battery thermal management system (BTMS). In this article, the Z-type air cooling BTMS is studied, and a method combining an artificial neural network (ANN) model as a surrogate model with non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization is proposed to enhance the heat dissipation performance. The ANN model is trained by using 1529 sets of numerical simulation data of Z-type air cooling system, and the fast and high-precision prediction of heat dissipation performance is realized, which effectively avoids the repeated calculation of CFD. The design variables studied include geometric structure parameters and inlet wind speed (vinlet), and the target variables are maximum temperature (Tmax), temperature difference (ΔTmax) and pressure loss (ΔP). From the new perspective of genetic evolution process of NSGA-II, it is proved that the design of tapered inlet and outlet is better than that of flat and expansion outlet, the wide channel near the inlet and narrow channel near the outlet are more conducive to the improvement of heat dissipation performance. Further analysis shows that there is a linear relationship among vinlet, Tmax and ΔP in the Pareto solution set of Z-type air-cooling system, which simplifies the Pareto model effectively, and engineers can predict the optimal performance of BTMS by this relationship. In addition, through sensitivity analysis, it is found that in the Z-type BTMS, the operating parameter vinlet has the greatest influence on the heat dissipation performance and pressure loss, while the geometric parameters have less influence. Compared with the benchmark case, the optimized design scheme reduces Tmax, ΔTmax and ΔP by 1.97 K, 6.32 K and 2.82 Pa, respectively, which significantly improves the heat dissipation performance.

Multi-objective size optimization and economic analysis of a hydrogen-based standalone hybrid energy system for a health care center

The main contribution of this study is the multi-objective size optimization of a standalone hybrid energy system (HES) to supply the electricity demand of a health care center in Kerman province, south of Iran, in which fuel cell, photovoltaic, and diesel generator produces electricity, while the combination of electrolyzer and hydrogen tank is the storage system. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to find the Pareto front of optimal solutions, where the loss of power supply probability (LPSP) and total net present cost (TNPC) are the objective functions. The results show that considering operating reserve and uncertainty, the TNPC increases 30.50% compared to the base load. Moreover, the fuel cell has a smaller share of energy production, which is due to the high cost of the storage system, while about 50% of the demand load is provided by DG in the cold seasons. The sensitivity analysis reveals that TNPC decreases by 8.93% for a 50% reduction of the storage system cost, while increases by 24.55% for a 50% increase in the fuel cost. Moreover, AHP/TOPSIS-based multi-criteria decision-making is implemented to find the best solution according to the opinions of three different managers/experts, which shows that the HES with LPSP = 0.2503% and TNPC = 2.1038 × 106 $ is the best solution in which uncertainties and operating reserve are also considered.

Cooperative Application of Onboard Energy Storage and Stationary Energy Storage in Rail Transit Based on Genetic Algorithm

The transition towards environmentally friendly transportation solutions has prompted a focused exploration of energy-saving technologies within railway transit systems. Energy Storage Systems (ESS) in railway transit for Regenerative Braking Energy (RBE) recovery has gained prominence in pursuing sustainable transportation solutions. To achieve the dual-objective optimization of energy saving and investment, this paper proposes the collaborative operation of Onboard Energy-Storage Systems (OESS) and Stationary Energy-Storage Systems (SESS). In the meantime, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to optimize the ESS capacity and reduce its redundancy. The simulation is programmed in MATLAB. The results show that the corporation of OESS and SESS offers superior benefits (70 kWh energy saving within 30 min operation) compared to using SESS alone. Moreover, the OESS plays a significant role, emphasizing its significance in saving energy and investment, therefore presenting a win–win scenario. It is recommended that the capacity of OESS be designed to be two to three times that of SESS. The findings contribute to the ongoing efforts in developing more sustainable and energy-efficient transportation solutions, with implications for the railway industry’s investment and broader initiatives in energy saving for sustainable urban mobility.

Multi-objective optimization of clustered wind farms based on potential game approach

With the rapid development of offshore wind power, the construction and operation of multiple adjacent wind farms (WFs) in the form of cluster has become a trend. However, the method for single WF planning can not address the WF cluster layout optimization in the complex relationship of competition or cooperation. This paper proposes a novel method to design clustered offshore WFs. The objectives to be optimized include the maximization of WF generated power and the minimization of wind turbine (WT) turbulence intensity. To solve this problem, a mathematical model based on the potential game theory is established. In this planning model, each WF in the cluster is assumed to be an individual stakeholder, the decision variables and the constraints are mapped to be the strategies and the strategy sets of the stakeholders, respectively, and the objectives are set as their payoffs. The non-dominated sorting genetic algorithm II (NSGA-II) is synthesized with the dependency structure matrix genetic algorithm II (DSMGA-II) to tackle the multi-objective optimization problem (MOOP) and the fuzzy-membership function (FMF) method is used to select the final solution from the Pareto optimal set. The best-strategy-response iterative algorithm is put forward to search for the Nash equilibrium (NE) of the game. Case studies on three clustered offshore WFs in Qidong, China, are carried out to validate the effectiveness of the proposed approach. Also, the simulation results indicate that the synthesized algorithm outperforms the NSGA-II in solving this optimization problem.

Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment.

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.

Multi-Objective Operating Parameters Optimization for the Start Process of Proton Exchange Membrane Fuel Cell Stack with Non-Dominated Sorting Genetic Algorithm II

Proton exchange membrane fuel cell (PEMFC) is one of the most promising energy conversion devices in the world. Performance, durability, and cost are the key issues currently limiting its large-scale commercial application. This paper proposes a non-dominated sorting genetic algorithm II (NSGA-II) for optimizing operating parameters of the start process to improve the output performance of a PEMFC stack. First, a Simulink model of the PEMFC stack including the anode module, cathode module, water transfer module, output voltage module, and output net power module is established, and the accuracy of the stack model is verified through experiments. The three performances are then optimized simultaneously based on NSGA-II. The results show that the optimized operating parameters for the start process results in a PEMFC stack that outperforms the base case in steady-state voltage, percentage of undershoot, and net power these three indicators with the same response time, demonstrating the success of the method in solving multiple optimization problems. This study presents an effective approach for the multi-objective optimization of the PEMFC stack, which is of guidance for engineering practice.