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Data-driven model-based calibration for optimizing electrically boosted diesel engine performance

INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2023年第4期24卷 1515-1529页

作者： Pal, Anuj Zhu, Ling Wang, Yan Zhu, Guoming Michigan State Univ E Lansing MI 48824 USA Ford Motor Co Dearborn MI 48121 USA

Engine calibration is a process of optimizing its control variables, which is a crucial step to achieve the desired performance from the engine system. However, its complexity has increased dramatically due to the constant evolution of the engine architecture with multiple control variables interacting with each other, making it difficult to calibrate its control variables with significantly increased experimental costs. System complexity also affects the physics-based modeling process as it makes the system model highly non-linear and difficult to capture system dynamics under all engine operating conditions. To overcome these challenges, the current work proposes a machine learning-based framework to reduce the required calibration effort. The work implements a stochastic surrogate assisted optimization approach on an actual engine experimental test setup, which efficiently looks for the global optimal solution based on only the input-output data set. This work performs a steady-state calibration of a turbocharged diesel engine with an additional external electric-assisted boosting system (eBoost). Three control parameters are considered for this study: Variable Geometry Turbocharger (VGT) vane position, Exhaust Gas Recirculation (EGR) valve position, and eBoost speed to identify the optimal trade-off between engine efficiency (Brake Specific Fuel Consumption) and emissions (NO x ). The addition of eBoost makes the calibration problem difficult because the two control parameters, VGT and EGR, also depend on eBoost speed. Using the proposed data-driven calibration approach, the experimental results showed enhanced engine performance in terms of efficiency and emissions using the eBoost compared with the case without eBoost. At the same time, the experimental cost was reduced significantly due to the reduced number of testing points required to achieve the optimum.

关键词： Engine calibration stochastic optimization surrogate model constrained multi-objective optimization data-driven control

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A Self-Adaptive Evolutionary multi-Task Based constrained multi-objective Evolutionary Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1098-1112页

作者： Qiao, Kangjia Liang, Jing Yu, Kunjie Wang, Minghui Qu, Boyang Yue, Caitong Guo, Yinan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China China Univ Min & Technol Beijing 100084 Peoples R China

constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.

关键词： Task analysis optimization Statistics Sociology Knowledge transfer multitasking Genetic algorithms constrained multi-objective optimization evolutionary multi-task optimization knowledge transfer self-adaptive intra-task inter-task

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An approach of multi-objective computing task offloading scheduling based NSGS for IOV in 5G

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第6期25卷 4203-4219页

作者： Zhang, Jie Piao, Ming-jie Zhang, De-gan Zhang, Ting Dong, Wen-miao Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligent Comp Novel Software T Tianjin 300384 Peoples R China Tianjin Univ Sport Sch Sports Econ & Management Tianjin 301617 Peoples R China

As a new technology, Internet of Vehicles (IoV) needs high bandwidth and low delay. However, the current on-board mobile terminal equipment cannot meet the needs of the IoV. Therefore, using mobile edge computing (MEC) can solve the problems of energy consumption and time delay in the IoV. In the MEC, task offloading can solve the problem of resource constraint on mobile devices effectively, but it is not optimal to offload all tasks to edge servers. In this paper, the vehicle computation task is regarded as a directed acyclic graph (DAG), and task nodes' execution location and scheduling order are optimized. Considering the energy consumption and delay of the system, the vehicle computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then a Non-dominated Sorting Genetic Strategy(NSGS) is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay and energy consumption. Finally, a large number of experiments are carried to prove the performance of the algorithm. The experimental results show that the algorithm can make the optimal decision in practical applications.

关键词： Internet of vehicles Mobile edge computing Computation offloading Task segmentation constrained multi-objective optimization NSGS

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An augmented Lagrangian algorithm for multi-objective optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2020年第1期77卷 29-56页

作者： Cocchi, G. Lapucci, M. Univ Firenze Dept Informat Engn Via Santa Marta 3 I-50139 Florence Italy

In this paper, we propose an adaptation of the classical augmented Lagrangian method for dealing with multi-objective optimization problems. Specifically, after a brief review of the literature, we give a suitable definition of Augmented Lagrangian for equality and inequality constrained multi-objective problems. We exploit this object in a general computational scheme that is proved to converge, under mild assumptions, to weak Pareto points of such problems. We then provide a modified version of the algorithm which is more suited for practical implementations, proving again convergence properties under reasonable hypotheses. Finally, computational experiments show that the proposed methods not only do work in practice, but are also competitive with respect to state-of-the-art methods.

关键词： constrained multi-objective optimization Augmented Lagrangian method Global convergence Pareto stationarity multi-objective steepest descent

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Performance optimization of hard rock tunnel boring machine using multi-objective evolutionary algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期169卷

作者： Fan, Zhun Zheng, Zehao Xu, Biao Li, Wenji Zhang, Yonggang Hao, Zhifeng Shantou Univ Dept Elect Engn Shantou 515063 Peoples R China Hanshan Normal Univ Dept Phys & Elect Engn Chaozhou 521041 Peoples R China Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou 515063 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

The hard rock Tunnel Boring Machine (TBM) is a complex engineering equipment coupled with multiple sub-systems for underground tunnel excavation in complex geological environments. Resetting the operational and structural parameters of TBM according to different geological conditions usually requires engineers to spend a lot of time dealing with the interaction between various subsystems, which is a tedious and time-consuming job. To facilitate setting the operational and structural parameters of TBM, we present a con-strained multi-objective optimization model and its solving method in this paper. To be specific, three perfor-mance indices, i.e. minimizing the system construction period, construction energy consumption and construction cost of TBM, are firstly considered as the three objectives in the proposed model. Secondly, two push and pull search (PPS) based algorithms, including PPS-MOEA/D and PPS-KnEA, are suggested to solve the formulated constrained multi-objective optimization problem. Finally, to verify the performance of the devel-oped method, the presented method is compared with several popular constrained multi-objective evolutionary algorithms by tackling the established optimization model. The experimental results reveal that the presented method has the best performance among the comparison algorithms, and the overall performance of the algo-rithm with PPS is better than other algorithms without PPS, which indicates the superiority of PPS framework in solving practical optimization problems.

关键词： constrained multi-objective optimization TBM performance optimization TBM modeling

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Design Recommendations for Minimal Antenna Mutual Coupling Using Current optimization 18

Design Recommendations for Minimal Antenna Mutual Coupling U...

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18th European Conference on Antennas and Propagation (EuCAP)

作者： Bartle, Hannes Liska, Jakub Capek, Miloslav Skrivervik, Anja K. Jelinek, Lukas Ecole Polytech Fed Lausanne Microwaves & Antennas Grp Lausanne Switzerland Czech Tech Univ Dept Electromagnet Field Fac Elect Engn Prague Czech Republic

ISBN: (纸本)9788831299091;9798350394436

This work addresses a first attempt at using current density optimization algorithms to gain insight into the relation between maximum achievable gain in a given direction and the mutual coupling to another antenna nearby. The methodology is presented on the example of the coupling between two patch antennas placed above an infinite ground plane. The optimized antenna gain is shown to have a strong relation to the geometry of the problem. The obtained insight is then formulated into design recommendations that can help in an actual design.

关键词： mutual coupling current optimization method of moments constrained multi-objective optimization antenna gain

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Pareto Optimality for Fairness-constrained Collaborative Filtering 21

Pareto Optimality for Fairness-constrained Collaborative Fil...

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29th ACM International Conference on multimedia (MM)

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Peng Cheng Lab Artificial Intelligence Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9781450386517

The well-known collaborative filtering (CF) models typically optimize a single objective summed over all historical user-item interactions. Due to inevitable imbalances and biases in real-world data, they may develop a policy that unfairly discriminates against certain subgroups with low sample frequencies. To balance overall recommendation performance and fairness, prevalent solutions apply fairness constraints or regularizations to enforce equality of certain performance across different subgroups. However, simply enforcing equality of performance may lead to large performance degradation of those advantaged subgroups. To address this issue, we formulate a constrained multi-objective optimization (MOO) problem. In contrast to the single objective, we treat the performance of each subgroup equivalently as an objective. This ensures that the imbalanced subgroup sample frequency does not affect the gradient information. We further propose fairness constraints to limit the search space to obtain more balanced solutions. To solve the constrained MOO problem, a gradient-based constrained MOO algorithm is proposed to seek a proper Pareto optimal solution for the performance trade-off. Extensive experiments on synthetic and real-world datasets show that our approach could help improve the recommendation accuracy of disadvantaged groups, while not damaging the overall performance.

关键词： Pareto Optimal constrained multi-objective optimization Collaborative Filtering

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Evolutionary Antenna Design via Modified Normalized GT Algorithm

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3rd International Conference on Intelligence Computation and Applications

作者： Fan, Yuanyuan Liang, Qingzhong Cai, Zhenhua Li, Hui Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9783540921363

Jason Lohn and his workmates in NASA Ames Research Center have evolved an X-band antenna for NASA's Space Technology 5 (ST5) mission which is the first evolved hardware in space, However their approach did not Put too much attention on the efficiency of the evolutionary algorithm. Owing to the flaw we employ GT Algorithm to tackle constraints and balance multi-objective via normalization which makes evolution more efficient. Moreover we adopt a kind of linear real-values code to describe the structure of antenna so that it is easier to carry out genetic operations and control the size of the antenna. We have evolved a wire antenna successfully via this approach and all the targets have been meted.

关键词： evolutionary antenna design constrained multi-objective optimization evolutionary algorithm GT algorithm

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Decision Making for Environmental/Economic Dispatch Based on Optimal Power Flow 8

Decision Making for Environmental/Economic Dispatch Based on...

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8th IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

作者： Xiao, Chixin Sutanto, Danny Muttaqi, Kashem M. Zhang, Minjie Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW Australia Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW Australia

ISBN: (纸本)9781538693155

This paper proposes the use of optimal power flow (OPF) to solve a multi-objective optimization problem (MOP) of environmental/economic dispatch (EED). Firstly, by using an aggregate objective function (AOF) with a set of uniform weight vectors, the EED problem is decomposed into a group of scalar sub-problems, each of them is aggregated into a single objective. The uniform weight vectors represent different trade-off levels between each objective (i.e. fuel cost and emission). Secondly, each sub-problem is optimized by the similar numerical analysis of OPF rather than heuristic algorithms, while taking into account the losses in the transmission lines, power balance and the generating unit active and reactive power constraints precisely. Thus, an accurate optimum can he obtained for each sub-problem rather than a reasonable optimal solution every time. Further, each optimum is then associated with a concrete weight vector which can be treated as decision maker (DM)'s preference for selecting the final preferred solution. The simulation results based on the IEEE 30-bus system have shown that the obtained solutions are accurate and stable, thus they can be used as benchmark to test other methods as well.

关键词： constrained multi-objective optimization environmental/economic dispatch nonlinear optimization weighted sum decomposition

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A Decomposition-based constrained multi-objective Evolutionary Algorithm with An Infeasibility Utilization Mechanism for UAV Path Planning 34

A Decomposition-based Constrained Multi-objective Evolutiona...

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34th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Cai Zhang, Jinpeng Huang, Lin Peng, Chaoda South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

ISBN: (纸本)9781665478960

Evolutionary algorithm has been used to solve unmanned aerial vehicle (UAV) path planning problems. Usually, a UAV path planning problem is modelled as a constrained optimization problem, and the goal for evolutionary algorithms is to find a collision-free trajectory with respect to the constraints, which requires the algorithms equipping not only a powerful search engine but an effective constraint-handling technique. In the community of constrained multi-objective evolutionary algorithms, an essential idea is how to make good use of the informative infeasible solutions during the evolution process, which can significantly improve performance of the algorithms. To the best of our knowledge, this has seldom been explored in UAV path planning problems. To address this issue, this paper proposes a decomposition-based constrained multi-objective evolutionary algorithm with an infeasibility utilization mechanism for solving UAV path planning problem. UAV path planning represented by the B-Spline curve is first formulated as a bi-objective optimization problem, i.e., minimizing the travelling distance and the risk of a UAV, with three constraints including the minimum flight height, the maximum flight height and minimum flight angle. Then a decomposition-based constrained multi-objective evolutionary algorithm that can utilize the information containing in infeasible solutions is adopted to solve the constrained optimization problem. To further make good use of the infeasible solutions found by the algorithm, an infeasible utilization mechanism is proposed to guide the search to the optimal regions. The experimental results have indicated that the proposed algorithm is superior over the compared algorithm in terms of finding a set of well-distributed and well-converged non-dominated solutions.

关键词： UAV Path Planning constrained multi-objective optimization Evolutionary Algorithm Constraint-handling Technique

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