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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 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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On optimality and duality for multiple-objective optimization under generalized type I univexity

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2009年第8期86卷 1345-1354页

作者： Soleimani-damaneh, M. Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran

In this paper, we deal with the multiple-objective optimization problems, considering an improved definition of generalized type I univex function. Some optimality conditions as well as some duality relations are esta... 详细信息

关键词： optimality duality multiple-objective optimization generalized type I univexity Pareto optimality

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GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

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COMPUTERS & OPERATIONS RESEARCH 2012年第9期39卷 1951-1968页

作者： Zinflou, Arnaud Gagne, Caroline Gravel, Marc Inst Rech Hydro Quebec IREQ Varennes PQ J3X 1S1 Canada Univ Quebec Chicoutimi Dept Sci Econ & Adm Chicoutimi PQ G7H 2B1 Canada Univ Quebec Chicoutimi Dept Informat & Math Chicoutimi PQ G7H 2B1 Canada

In this paper, we propose a new Pareto generic algorithm, called GISMOO, which hybridizes genetic algorithm and artificial immune systems. GISMOO algorithm is generic in the sense that it can be used to solve both combinatorial and continuous optimization problems. The proposed approach offers an original iterative process in two phases: a Genetic Phase and an Immune Phase. The Immune Phase is used to identify and to emphasize the solutions located in less crowded regions found during the iterative process of the algorithm. Simulation results on difficult test problems, both in combinatorial and continuous optimization, show that the proposed approach, in most problems, is able to obtain better results than state of the art algorithms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Evolutionary algorithm Artificial immune systems multiple-objective optimization Hybridization MOKP ZDT

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Nonsmooth multiple-objective optimization in separable Hilbert spaces

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2009年第10期71卷 4553-4558页

作者： Soleimani-damaneh, M. Nieto, J. J. Inst Res Fundamental Sci IPM Sch Math Tehran Iran Univ Tehran Sch Math Stat & Comp Sci Univ Coll Sci Tehran Iran Univ Santiago de Compostela Fac Matemat Dept Anal Matemat Santiago De Compostela Spain

关键词： Limiting subdifferential Generalized differentiation multiple-objective optimization Separable Hilbert space

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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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Solving multiple-objective optimization problems using GISMOO algorithm

Solving multiple-objective optimization problems using GISMO...

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World Congress on Nature and Biologically Inspired Computing

作者： Zinflou, Arnaud Gagne, Caroline Gravel, Marc Univ Quebec Chicoutimi Dept Math & Informat Chicoutimi PQ Canada

ISBN: (纸本)9781424450534

In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimization (MOKP). Results show that our approach is able to obtain better performance than two state of the art approaches: NSGAII and PMSMO.

关键词： evolutionnary algorithm artificial immune systems multiple-objective optimization hybridization Pareto front MOKP

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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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Optimality and Duality for Nondifferentiable multiple-objective optimization with Generalized Univexity

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Journal of Systems Science and Systems Engineering 2001年第2期13卷 162-172页

作者： ZHOU Hou-chun 1, QIU Yong-ping 21.Department of Mathematics, Shandong Linyi Teacher′s College, Shandong 276005, China2.Department of Mathematics, Shandong Jinan Educational College, Jinan 250002, China Department of Mathematics Shandong Linyi Teacher's College Shandong 276005 China Department of Mathematics Shandong Jinan Educational College Jinan 250002 China

Using generalized univex functions, a nondifferentiable multiple-objective optimization problem is ***-Tucker type sufficient optimality conditions are obtained for a feasible point to be an efficient or properly effi... 详细信息

关键词： multiple-objective optimization efficient solutions properly efficient solutions

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