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A Simulation-Based multiple-objective optimization for Designing K-Stacks Autonomous Valet Parking Lots

JOURNAL OF ADVANCED TRANSPORTATION 2025年第1期2025卷

作者： Zhang, Chu Xue, Shaopei Chen, Jiayi Chen, Jun Southeast Univ Sch Transportat Nanjing Peoples R China

Autonomous valet parking has drawn wide attention these years. The k-stacks layout, known for its ability to increase parking capacity by stacking vehicles more compactly, is of great practicality among all possible layout patterns. Although this layout can increase the capacity of a parking lot, it generates relocations, which let vehicles move additional distances and influence the lot's peak hour service ability. For the sake of optimizing them all simultaneously, we propose a simulation-based multiple-objective optimization (SMOO) and use NSGA II to solve the problem, obtaining candidate solutions. Then, a nondominated sorting based on cumulative advantages (NSCA) method is put forward to select the most robust solution from all candidates, considering different demand scenarios. K-stacks parking lots optimized by the SMOO can provide 36%-59% more parking spaces than a traditional parking lot while keeping other evaluations fine. In addition, we specify high-demand and low-demand scenarios and discuss the impact of different aspect ratios. It is recommended to use k-stacks layouts when a lot's length is close to its width.

关键词： autonomous valet parking layout cumulative advantages multiple-objective optimization NSGA II

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multiple-objective optimization of Jet Controlled Compression Ignition (JCCI) mode with dual-direct injection in a high-speed light-duty engine

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FUEL 2023年第Part1期333卷

作者： Li, Bo Wang, Jian Zhang, Heng Fan, Liyun Leng, Xianyin Long, Wuqiang Xiao, Ge Harbin Engn Univ Coll Power & Energy Engn Harbin Peoples R China Dalian Univ Technol Sch Energy & Power Engn Dalian Peoples R China Jiangsu Univ Inst Energy Res Zhenjiang Peoples R China Dalian Univ Technol Sch Energy & Power Engn Dalian Peoples R China

Jet Controlled Compression Ignition (JCCI) mode with dual-direct injection of diesel and gasoline has been demonstrated by the engine experiments to provide an effective control on the combustion process without sacrificing the advantages on efficiency and emissions performance. The multiple-objective optimization of JCCI mode based on the 186FA engine at 75 % engine load with an engine speed of 3000 r/min was carried out in this research by the combination of Three-Dimensional (3D) Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA). The simulation results indicate that the ignition timing and the combustion phasing can be effectively controlled by the jet-injection diesel, and the pre-injection gasoline mainly affects the combustion stage from CA50 to CA90. With the increase of the pre-injection energy ratio, higher initial in-cylinder temperature is required to ensure the engine performance. The optimal results present that JCCI mode can achieve high efficiency and low emissions simultaneously when the combustion phasing is at 5 similar to 9 degrees CA ATDC. The low EISFC, NO x and soot emissions can be realized when SOIpre and SOI(jet )are retarded to -63 and -28 degrees CA ATDC, respectively, which prepares the appropriate premixed charge considering the equivalence ratio and reactivity. The comparisons of the typical cases reveal that the high-temperature combustion process gradually translates to the two-stage characteristics when the SOIj et retards from -27.77 to -18.88 degrees CA ATDC, the combustion duration is prolonged slightly and the in-cylinder temperature is decreased, resulting in the reduction of Amax by 0.42 MPa, NOx emissions by 0.07 g/kWh, associating with the lower heat transfer loss and exhaust loss. In addition, the NOx emissions in JCCI mode are mainly generated within the high-temperature combustion region of the jet-injection diesel spray target location.

关键词： Jet Controlled Compression Ignition (JCCI) Dual-direct injection multiple-objective optimization Control parameter Engine performance

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Test Input Selection for Deep Neural Network Enhancement Based on multiple -objective optimization 30

Test Input Selection for Deep Neural Network Enhancement Bas...

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30th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Hao, Yao Huang, Zhiqiu Guo, Hongjing Shen, Guohua Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665452786

Deep Neural Networks (DNNs) have been applied in many domains, such as autonomous driving and image recognition. However, due to the lower-than-expected performance of the DNN models in the real application, researchers are committed to sampling a test subset from test data with the limited labeling effort to retrain the DNN models for enhancement. Existing test input selection methods aim at selecting the test inputs according to the probability that is classified incorrectly by the DNN model. However, the test inputs selected by using existing methods might have similar features, making the DNN model unable to learn more diverse features when retraining. To address this limitation, this paper proposes multiple-objective optimization-Based Test Input Selection (MOTS) to select more effective test subset to retrain the DNN model for enhancement. Different from existing works, this work not only considers the uncertainty of the test input but also takes the diversity of the test subset into account. Then MOTS uses a multiple-objective optimization algorithm NSGA-II to solve this problem, which ensures the test subset has more diverse features and is more helpful for retraining DNN models. This paper conducts the experiment on two popular DNN models and three widely-used datasets. The experiment results indicate that MOTS achieves 114 %, 72%, 55%, 41% average accuracy improvement under four different sampling ratios 1%, 3 %, 5%, 10% compared with five baseline methods. Therefore, MOTS is very effective in improving the quality of the DNN models compared with the state-of-the-art methods and the diversity of the test subset contributes greatly to the effectiveness of retraining DNN models.

关键词： deep neural network testing test input selection retraining multiple-objective optimization

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multiple-objective optimization for Solar Concentrator Layout

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2020年第1期142卷 1-11页

作者： Wang, Kung-Jeng Dao, Le Duc Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan

Solar energy is critical because it does not only sustain global environment but also produce ample power to use. This study addresses solar concentrator layout to maximize the profit for the firm manufacturing the concentrator, while helping an energy user receives as much sunlight as possible. We consider several key design factors of a solar concentrator layout, such as light transmission loss, to make the model more accurate. As for the economic scale of production, some key constraints are considered, such as concentrator thickness and the number of exits, where a sunbeam is delivered to an optical cable for energy transmission. To obtain a high brightness from a good solar concentrator for the user, the concentrator manufacturer requires to increase the complexity of concentrator layout, but suffers from the high manufacturing cost. In this study, we simultaneously address the issues of the optimal solar concentrator layout and trade-off of conflict objectives between the energy user and the concentrator manufacturer. This study proposes a nondominated sorting genetic algorithm for multiple-objective optimization solution. To summarize, the result from this study presents a promising solution both for the light efficiency to supply users and for the profit of the firm on delivering the solar concentrator layout.

关键词： genetic algorithm multiple-objective optimization solar concentrator layout renewable solar receiver

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A chiller operation strategy based on multiple-objective optimization

A chiller operation strategy based on multiple-objective opt...

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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Qiu, Shunian Zhang, Weijie Li, Jiajie Chen, Jialiang Li, Zhenhai Li, Zhengwei Tongji Univ 1239th Siping Rd Shanghai 200092 Peoples R China

30%40% energy consumption of the HVAC system is caused by chillers. The energy consumption of chillers highly depends on the quality of the operation strategy. A chiller operation strategy based on multiple-objective optimization is proposed in this paper. The strategy consists of three steps: (1) With effectiveness functions, two indicators of the operation goodness, indoor comfort and energy consumption, are quantified to two indicators (2) These two indicators are integrated to one comprehensive indicator. (3) The optimal operation condition is determined by an optimization algorithm to maximize the comprehensive indicator. The parameters in this strategy is defined according to on-site surveys, to improve the application value of the strategy. The effectiveness of the proposed strategy is validated on TRNSYS compared with the 3 common operation strategies adopted in buildings of Shanghai. The simulation results suggest that the proposed strategy is able to save energy of HVAC system with limited loss of indoor comfort. Besides, due to the appropriate arrangement of operation order, the proposed strategy could balance the working time of each chiller to put off the discard of chillers. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Chiller Effectiveness functions Energy simulation multiple-objective optimization

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A chiller operation strategy based on multiple-objective optimization

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Energy Procedia 2018年 152卷 318-323页

作者： Shunian Qiu Weijie Zhang Jiajie Li Jialiang Chen Zhenhai Li Zhengwei Li

30%-40% energy consumption of the HVAC system is caused by chillers. The energy consumption of chillers highly depends on the quality of the operation strategy. A chiller operation strategy based on multiple-objective optimization is proposed in this paper. The strategy consists of three steps: (1) With effectiveness functions, two indicators of the operation goodness, indoor comfort and energy consumption, are quantified to two indicators (2) These two indicators are integrated to one comprehensive indicator. (3) The optimal operation condition is determined by an optimization algorithm to maximize the comprehensive indicator. The parameters in this strategy is defined according to on-site surveys, to improve the application value of the strategy. The effectiveness of the proposed strategy is validated on TRNSYS compared with the 3 common operation strategies adopted in buildings of Shanghai. The simulation results suggest that the proposed strategy is able to save energy of HVAC system with limited loss of indoor comfort. Besides, due to the appropriate arrangement of operation order, the proposed strategy could balance the working time of each chiller to put off the discard of chillers.

关键词： Chiller Effectiveness functions Energy simulation multiple-objective optimization

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multiple-objective Packet Routing optimization for Aeronautical Ad-Hoc Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第1期72卷 1002-1016页

作者： Zhang, Jiankang Liu, Dong Chen, Sheng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos Bournemouth Univ Dept Comp & Informat Poole BH12 5BB England Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete $\epsilon$ multi-objective genetic algorithm ($\epsilon$-DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our $\epsilon$-DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

关键词： Aircraft mobility model aeronautical ad-hoc network adaptive coding and modulation routing multiple-objective optimization

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Multi-objective optimization of the aerodynamic performance of butterfly-shaped film cooling holes in rocket thrust chamber

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2025年第1期42卷 159-169页

作者： Yu, Ningning Wang, Zhongwei Lu, Yuming Xu, Weicheng Shan, Yiming Zhang, Hai Harbin Engn Univ Harbin 150001 Peoples R China

This study uses Multi-Island Genetic Algorithm (MIGA) and three-dimensional Computational Fluid Dynamics (CFD) software to optimize butterfly-shaped film cooling holes in the upper-stage rocket engine thrust chamber. The goal is to meet thermal protection and thrust requirements at high altitudes without re-ignition. To facilitate an all-encompassing worldwide search, the holes in the optimized design remain at set dimensions. Film continuity and stability at the nozzle outlet are greatly impacted by the hole structure. Inlet and divergence angles have little effect on thrust, according to regression research, but lip height (de) and outlet width (beta) have a big impact on cold gas ejection, which affects cooling and thrust. Optimized results lead to a 20.49 K decrease in the monitoring section's average wall temperature and a 52.8 N boost in thrust by reducing interference between supersonic airflow and extending film stability.

关键词： multiple-objective optimization film cooling butterfly-shaped film cooling hole temperature thrust

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Integrated velocity optimization and energy management for FCHEV: An eco-driving approach based on deep reinforcement learning

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ENERGY CONVERSION AND MANAGEMENT 2023年 296卷

作者： Chen, Weiqi Peng, Jiankun Ren, Tinghui Zhang, Hailong He, Hongwen Ma, Chunye Southeast Univ Sch Transportat Nanjing 211102 Peoples R China North Univ China Coll Mech Engn Taiyuan 030051 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Ecological driving (eco-driving) is a promising technology for transportation sector to save energy and reduce emission, which works by improving vehicle behaviors in traffic scenarios. Fuel cell hybrid electric vehicles (FCHEV) are receiving extensive attentions due to global fossil energy crisis, but whose implementations for eco-driving result in multiple objective collaborative optimization problems. In this paper, an eco-driving framework for FCHEV is proposed based on deep deterministic policy gradient (DDPG) algorithm. And it combines adaptive cruise control (ACC) and energy management strategy (EMS) into an integrated architecture. Firstly, in order to achieve excellent balance between driving behaviors and fuel economy, an appropriate weight coefficient value is determined after adequate explorations. Secondly, power-varying equivalent hydrogen conversion coefficient function is constructed to save fuel consumption by 8.97%. Thirdly, ablation experiments for health state of fuel cell system present 19.95% decrease in terms of health degradation. Then, comparison experiments indicate that the DDPG-based eco-driving strategy can reach 94.16% of that of dynamic programming with respect to equivalent hydrogen consumption, meanwhile with best ride comfortability. Moreover, simulation results under validation driving cycle manifest its excellent adaptability.

关键词： Ecological driving Fuel cell hybrid electric vehicle multiple-objective optimization Deep deterministic policy gradient

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Grey relational analysis-based composite coating property optimization fabricated by laser cladding

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期120卷 7985-7996页

作者： Lian, Guofu Liu, Zhaozhen Zhang, Yang Que, Linzhi Chen, Changrong Yue, Kun Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350118 Peoples R China Western Carolina Univ Sch Engn Technol Cullowhee NC 28723 USA

This paper aims at optimizing the surface hardness and wear resistance of composite coatings. The influence of Ni35A proportions in the composite mixing with TiC, WC, and W6Mo5Cr4V2 were explored based on mixed-level orthogonal experiment design. Experiments were conducted to examine the hardness and friction. The worn morphology was analysed by the white light interferometer. The multi-response optimization was achieved by the grey correlation analysis. The results show that both the powder ratio and powder type had significant impact on the micro-hardness and wear volume of the coatings, where powder ratio has more significance. Finally, the multi-index optimization was conducted with the target of maximizing the micro-hardness and minimizing the wear volume simultaneously by combining grey relational analysis and orthogonal experiment. The error rate is 1.122% between the prediction and experimental validation. Compared with the substrate, the micro-hardness of optimized cladding layer was improved from 15.2 to 68.8 HRC, while the wear volume was reduced from 15.28257 to 1.04831 center dot 10(-3) mm(3). The comparison of the optimal group in the orthogonal experiment indicates that the micro-hardness and wear resistance of optimized cladding layer were also improved. The optimized cladding layer has fine, dense, and evenly distributed grains with coarse and complete grain boundaries, suggesting that the micro-hardness and wear resistance were improved after optimization. The application of the mixed-level orthogonal experiment design combined with the grey correlation analysis allows to achieve the optimal selection of various hard-phase composite materials and the process optimization.

关键词： Laser cladding Composite coating Grey relation grade Micro-hardness Wear resistance multiple-objective optimization

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