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Multi-objective optimization of counter-flow dew-point evaporative coolers for multi-scenario applications using non-dominated sorting genetic algorithm ii

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APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Li, Yongcai Jing, Chengjun Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China

As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs in design parameters and multi-objective contradictions, focusing on unilateral decision-making and localized optimization in fixed environments. Therefore, this study innovatively couples non-dominated sorting genetic algorithm ii with mathematical modeling. Based on MATLAB programming, a complex multi-objective optimization model of counter-flow dew-point evaporative cooler capable of parameter prediction, multi-scenario application and multi-dimensional optimization is developed. To reveal the driving mechanism of multivariable on performance parameters, comparative studies of single-objective optimization under three decision modes for two typical environments are reported. The results indicate that the better optimization is achieved by adopting five decision variables with a dew-point efficiency of 98.25 %. Compared with the original working condition, the cooling capacity and dew-point efficiency could be unilaterally increased by 128.73 % and 121.28 %, respectively, and the corresponding increment of space utilization rate reaches up to 56.39 % and 56.11 %. Subsequently, setting the cooling capacity, dew-point efficiency and fan energy consumption as the synergistic objective function, a trade-off optimization with multi-decision variables is performed for four different latitudes. The obtained Pareto frontier could flexibly invert the most potential structural and operation parameter recommendations. Especially in dry regions, a cooling capacity of 4800 W and a dew-point efficiency of 91.2 % could be realized. The method realizes the controllability of performance parameters and adjustability of energy-saving effect, which could provide a solution for the efficient design of cooling equipment.

关键词： Counter-flow dew-point evaporative cooling Model construction Multi-objective optimization non-dominated sorting genetic algorithm ii Performance improvement

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non-dominated sorting genetic algorithm ii and Particle Swarm Optimization for design optimization of Shell and Tube Heat Exchanger

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2022年第0期132卷 105896-105896页

作者： Sai, Juluru Pavanu Rao, B. Nageswara Vignans Fdn Sci Technol & Res Dept Mech Engn Guntur 522213 Andhra Pradesh India

Optimization methods are applied in Shell and Tube Heat Exchanger (STHE) design to reduce the cost of the device. Various existing optimization techniques such as Particle Swarm Optimization (PSO), Adaptive Range genetic algorithm (ARGA) are applied in the design of STHE. Existing optimization methods used in STHE design, have the limitation of lower convergence and easily trap into local optima. In this research, the hybrid method of non-dominated sorting genetic algorithm ii (NSGA ii) and PSO method is proposed to reduce the cost in STHE design. The NSGA ii method is applied to improve the exploration and PSO method is applied to improve exploitation of search process. The hybrid method has objective function of total cost and overall heat transfer of the model to improve the performance. The NSGA ii has strong exploration in the search due to the non dominated search process and also provides good convergence. The PSO method is applied in the best solution of NSGA ii and the PSO method has the advantage of strong exploitation that escapes from the local optima. The hybrid NSGA ii-PSO method is tested on three test cases and is compared with existing optimization methods to analyze its performance. The result shows that the hybrid NSGA ii-PSO method has a 4.85% lesser total cost in case 1 and 1.51% lesser total cost in case 2, when compared to the ARGA method.

关键词： Hybrid method non-dominated sorting genetic algorithm ii Optimization methods Particle Swarm Optimization Shell and Tube Heat Exchanger (STHE)

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Studying the Reporting Cells Planning with the non-dominated sorting genetic algorithm ii 17

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17th European Conference on Applications of Evolutionary Computation (EvpApplications)

作者： Berrocal-Plaza, Victor Vega-Rodriguez, Miguel A. Sanchez-Perez, Juan M. Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

ISBN: (纸本)9783662455234;9783662455227

This manuscript addresses a vital task in any Public Land Mobile Network, the mobile location management. This management task is tackled following the Reporting Cells strategy. Basically, the Reporting Cells planning consists in selecting a subset of network cells as Reporting Cells with the aim of controlling the subscribers' movement and minimizing the signaling traffic. In previous works, the Reporting Cells Planning Problem was optimized by using single-objective metaheuristics, in which the two objective functions were linearly combined. This technique simplifies the optimization problem but has got several drawbacks. In this work, with the aim of avoiding such drawbacks, we have adapted a well-known multiobjective metaheuristic: the non-dominated sorting genetic algorithm ii (NSGAii). Furthermore, a multiobjective approach obtains a wide range of solutions (each one related to a specific trade-off between objectives), and hence, it gives the possibility of selecting the solution that best adjusts to the real state of the signaling network. The quality of our proposal is checked by means of an experimental study, where we demonstrate that our version of NSGAii outperforms other algorithms published in the literature.

关键词： Reporting Cells Planning Problem Mobile location management Multiobjective optimization non-dominated sorting genetic algorithm ii

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Multi-objective optimization based on improved non-dominated sorting genetic algorithm ii for tolerance allocation of auto-body parts

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Advances in Mechanical Engineering 2017年第9期9卷 1-9页

作者： Xu, Sha Xing, Yanfeng Chen, Weifeng Automotive Engineering College Shanghai University of Engineering Science 333 Long Teng Road Songjiang District Shanghai 201620 China

The auto-body is usually composed of compliant sheet metals. The assembly variation is inevitable in the process of auto-body assembly. A reasonable tolerance allocation method for compliant sheet metal body parts is an important means to control assembly variation and improve the quality of automotive assembly. In this article, on the basis of tolerance-deviation model and tolerance-cost model, a multi-objective optimization model for tolerance allocation of auto-body is established. Part tolerance and manufacturing cost are regarded as design variables in the model. Then, orthogonal design and cumulative sorting strategy are proposed to modify the non-dominated sorting genetic algorithm ii. Finally, the process of tolerance allocation is demonstrated through an example of the rear lamp bracket assembly. The results show that the improved non-dominated sorting genetic algorithm ii algorithm has obvious optimization effect. © The Author(s) 2017.

关键词： multi-objective optimization non-dominated sorting genetic algorithm ii sheet metal parts Tolerance allocation tolerance-cost model

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Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm ii (NSGA-ii)

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ENERGY 2020年第0期196卷 117090-000页

作者： Lee, Ungki Park, Sudong Lee, Ikjin Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Korea Electrotechnol Res Inst Energy Convers Res Ctr Chang Won 51543 South Korea

The thermoelectric generator (TEG) is a promising technology for the exhaust heat recovery of automobiles and TEG optimization has been widely studied. However, previous TEG optimization studies did not consider variations in the TEG net power output caused by uncertain parameters in the TEG system. This paper introduces a robust design optimization (RDO) that maximizes the mean of the performance function while minimizing its variance, leading to an optimum design that is less sensitive to uncertainties in TEG systems. A surrogate model is used to reduce the computational cost and the non-dominated sorting genetic algorithm ii (NSGA-ii) is used to find a compromise solution. The standard deviation of the TEG net power output of the deterministic optimum design (93.51W) verifies that the uncertainty of TEG systems significantly affects the variation of the TEG net power output, indicating that the uncertainty should be considered in TEG optimization problems. The compromise solution guarantees stable and high TEG net power output compared to the deterministic optimum design stemming from existing TEG optimization studies. The results of a global sensitivity analysis using the Sobol index indicate that the inlet temperature of the hot fluid has the greatest impact on the TEG net power output. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Thermoelectric generator (TEG) Robust design optimization (RDO) Uncertain parameter non-dominated sorting genetic algorithm ii Surrogate model Global sensitivity analysis

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ANFIS-based non-dominated sorting genetic algorithm ii for scenario-based joint energy and reserves market clearing considering TCSC device

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第12期25卷 3349-3373页

作者： Sardou, Iman Goroohi Ameli, Mohammad Taghi Shahid Beheshti Univ AC Fac Elect & Comp Engn Tehran Iran

This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiency index is proposed for the best placement of TCSC under network contingency conditions. Traditional MMP methods such as direction scalarization and e-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm ii (NSGA-ii), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-ii (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-ii methods. These comparisons confirm the efficiency of the developed method. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： market clearing thyristor-controlled series compensator (TCSC) non-dominated sorting genetic algorithm ii adaptive neuro-fuzzy inference system (ANFIS)

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Optimal design of microvascular networks based on non-dominated sorting genetic algorithm ii and fluid simulation

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ADVANCES IN MECHANICAL ENGINEERING 2017年第7期9卷

作者： Li, Peng Liu, Yuan Zou, Tian Huang, Jinyong East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Jiangxi Peoples R China

The non-dominated sorting genetic algorithm ii is used to design the microvascular networks embedded in self-healing polymeric materials. And the fluid simulation software Fluent is used to validate the optimization result obtained by non-dominated sorting genetic algorithm ii. Two objective functions are considered, namely, the void volume fraction and flow efficiency. A total of 222 solutions are obtained, and the head loss is in the range of (3.88 x 10(-7) m, 2.36 x 10(-6) m), whereas the void volume fraction is in the range of (4.39%, 5.12%). The simulation velocities are close to optimization velocities. The average error rate of selected solutions (a), (b), and (c) is 22.6%, 26.4%, and 35.2%, respectively.

关键词： Self-healing microvascular network optimization non-dominated sorting genetic algorithm ii fluid simulation

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Multi-objective optimization of proton exchange membrane fuel cell performance based on a new leaf-shaped flow field

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 120卷 254-275页

作者： Zhu, Haixi Ma, Yinjie Zhang, Lingxu Liu, Hanwen E, Jiaqiang Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

Challenges such as cost, lifespan, and durability hinder the commercialization of proton exchange membrane fuel cell. Enhancing the performance of fuel cell systems and improving water-thermal management are crucial for optimizing the overall performance of fuel cell. This study developed a novel leaf-shaped flow field structure and conducted a comprehensive performance evaluation. The results showed that the leaf-shaped flow field outperforms the serpentine and parallel flow fields in terms of overall performance. The simulation results of central composite design are analyzed based on the response surface methodology. A response regression model of power density, system efficiency, average water content of membrane and maximum temperature difference of cathode catalyst layer was established. The reliability of the regression model was verified through analysis of variance. The non-dominated sorting genetic algorithm ii coupled with the regression model was used for multi- objective optimization. The optimal solution was obtained by combining the technique for order preference by similarity to ideal solution decision-making method. Compared to the basic model, the optimal solution showed improvements of 37.12 %, 38.18 %, 22.94 %, and 74.17 % in power density, system efficiency, average water content of the membrane, and the maximum temperature difference in the cathode catalyst layer, respectively.

关键词： Proton exchange membrane fuel cell Flow field design Response surface methodology Computational fluid dynamics Multi-objective optimization non-dominated sorting genetic algorithm ii

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Efficiency-safety coordination optimization in drilling process under complex formations

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NEUROCOMPUTING 2025年 630卷

作者： Wu, Xiao Lai, Xuzhi Hu, Jie Lu, Chengda Yang, Xiao Zhou, Yang Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China

Efficient and safe drilling process under complex formation conditions has significant economic value for drilling engineering. Rate of penetration (ROP) and drill-string vibration are two widely used objectives for optimizing the drilling efficiency and safety, yet a simultaneous optimization of both is still a challenge due to their strong coupling relationship. To address this issue, an artificial intelligence-based coordinated optimization strategy is proposed for real-time dual-objective optimization of the two targets. First, by exploiting the multivariate distribution characteristics of drilling parameters, a Gaussian mixture model is applied for formation identification. Then, an information entropy-based coupling coordination degree (CCD) model is proposed to quantify the coordination relationship, and constrain optimization objectives. Finally, a dual-objective optimization model with ROP and vibration, served as the optimization objectives, is constructed, and adaptive model constraints are designed for different formations. A real-time optimization strategy combining non-dominated sorting genetic algorithm ii and sliding window technique is developed to determine optimal operational parameters, and a CCD-based decision criterion is developed for the Pareto solution set. Experiments on industrial data from an actual drilling field indicate that the proposed strategy leads to significant gains in drilling performances, providing a practical solution for drilling engineering.

关键词： Coupling coordination degree Drill-string vibration Formation identification non-dominated sorting genetic algorithm ii Rate of penetration

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Physical-based surrogate model and its application in 3D-1D fusion optimization of a marine two-stroke engine

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APPLIED THERMAL ENGINEERING 2025年 271卷

作者： Han, Xiao Liu, Long Xia, Qian Liu, Dai Tian, Jianyi Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Peoples R China China Shipbldg Power Engn Inst Co Ltd Shanghai 200129 Peoples R China Ningbo Geely Royal Engine Parts Co Ltd Ningbo 315000 Peoples R China

Marine two-stroke low-speed engines face challenges of complex experimentation and high computational costs, making 1D-3D integrated simulation optimization using surrogate models combined with optimization algorithms a potential solution. However, existing surrogate models suffer from issues such as high data dependency and low interpretability. To address this, this study develops simplified physical-based surrogate models, including a scavenging multi-stage model, a scavenging swirl model, and a swirl mixing controlled combustion (SMCC) surrogate model, calibrated via genetic algorithm (GA). Validation against experimental data shows errors within 5%. These models are coupled to enable full-cycle performance simulation. Subsequent multiobjective optimization using non-dominated sorting genetic algorithm ii (NSGA-ii) reduces maximum pressure (Pmax) and maximum pressure rise rate (dPmax) by 3.23% and 2.06%, respectively, while improving Brake Thermal Efficiency (BTE) by 1.1%. The optimized surrogate model maintains consistency with CFD results, with errors within 5%, but requires significantly less computational time than traditional CFD-based optimization. Therefore, physical-based simplified surrogate models integrated with optimization algorithms provide an effective approach for system-level simulation and multi-objective optimization of marine two-stroke low-speed engines.

关键词： Marine two-stroke low-speed engines 1D-3D fusion optimization Simplified physical-based model non-dominated sorting genetic algorithm ii (NSGA-ii)

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