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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Wen, Yanling Tan, Kecheng Liuzhou Railway Vocational Technical College Guangxi Liuzhou China

ISBN: (纸本)9798350315455

In the face of the rapid growth of China's car ownership, the variety of vehicle types, and the increasingly complex structure and use conditions of modern cars, the majority of car manufacturers urgently need to further control the performance of cars in the process of car design and production, and effectively measure the performance of cars and analyse and process the results so that they have high practicality and economic benefits. The purpose of this paper is to study the vehicle performance evaluation and testing system based on multi-objective genetic algorithm. The indicators for the evaluation of the vehicle performance are determined, including vehicle dynamics, fuel economy, braking, handling stability and environmental friendliness. The fuzzy comprehensive evaluation method is used to establish the subordinate degree function of each factor, so as to obtain the scores of the vehicle performance evaluation, select the appropriate input and output parameters, and establish the evaluation model using the neural network of multi-objective genetic algorithm. As a result, the overall vehicle performance scores after the two sets of real-world tests were 79 and 80 respectively. © 2023 IEEE.

关键词： fuzzy comprehensive evaluation inspection system multi-objective genetic algorithm performance evaluation whole vehicle performance

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Workspace optimization of parallel robot by using multi-objective genetic algorithm

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High Technology Letters 2022年第4期28卷 411-417页

作者： WANG Jinhong LEI Jingtao School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444P.R.China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai 200444P.R.China

For the narrow workspace problem of the universal-prismatic-universal(UPU)parallel robotwith fixed orientation,a kind of multi-objective genetic algorithm is studied to optimize the robot’*** concept of the effective workspace and its solution method are *** effectiveworkspace height(EWH)and global condition number index(GCI)of Jacobi matrix are selected asthe optimized objective *** the robot in two different orientations,the geometric pa-rameters are optimized by the multi-objective genetic algorithm named non-dominated sorting geneticalgorithm II(NSGA-II),and a set of structural parameters is *** optimization results areverified by four indicators with the robot’s moving platform at different *** resultsshow that,after optimization,the fixed-orientation workspace volume,the effective workspace heightand the effective workspace volume increase by 32.4%,17.8%and 72.9%on average,*** decreases by 6.8%on average.

关键词： parallel robot multi-objective genetic algorithm workspace optimization

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Using a heuristic multi-objective genetic algorithm to solve the storage assignment problem for CPS-based pick-and-pass system

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ENTERPRISE INFORMATION SYSTEMS 2021年第9期15卷 1238-1259页

作者： Tu, Mengru Yang, Ming-Feng Kao, Sheng-Long Lin, Feng-Cheng Wu, Ming-Hung Lin, Cheng-Kuan Natl Taiwan Ocean Univ Dept Transportat Sci Keelung Taiwan Natl Taiwan Ocean Univ Ctr Excellence Ocean Engn Keelung Taiwan Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan Natl Audit Off Taipei Taiwan

With the advancement of AI and the widespread application of IoT and cloud computing, the pick-and-pass system (PKPS) can potentially be transformed into a cyber-physical system (CPS) based intelligent warehouse picking system. This paper proposes a CPS-based PKPS with a heuristic multi-objective genetic algorithm to solve the NP-hard storage assignment problem (SAP) for order picking operations in an e-commerce-based warehouse. The proposed algorithm considers both the workload balance between picking lines and emergency replenishment during picking operation. Finally, the study shows that the proposed algorithm is effective in improving the efficiency of picking operations based on software simulation.

关键词： Cyber-physical system storage assignment problem pick-and-pass system multi-objective genetic algorithm computer-aided picking systems

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Feature selection with multi-objective genetic algorithm based on a hybrid filter and the symmetrical complementary coefficient

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APPLIED INTELLIGENCE 2021年第6期51卷 3899-3916页

作者： Zhang, Rui Zhang, Zuoquan Wang, Di Du, Marui Beijing Jiaotong Univ Sch Sci Beijing Peoples R China

With the expansion of data size and data dimension, feature selection attracts more and more attention. In this paper, we propose a novel feature selection algorithm, namely, Hybrid filter and Symmetrical Complementary Coefficient based multi-objective genetic algorithm feature selection (HSMOGA). HSMOGA contains a new hybrid filter, Symmetrical Complementary Coefficient which is a well-performed metric of feature interactions proposed recently, and a novel way to limit feature subset's size. A new Pareto-based ranking function is proposed when solving multi-objective problems. Besides, HSMOGA starts with a novel step called knowledge reserve, which precalculate the knowledge required for fitness function calculation and initial population generation. In this way, HSMOGA is classifier-independent in each generation, and its initial population generation makes full use of the knowledge of data set which makes solutions converge faster. Compared with other GA-based feature selection methods, HSMOGA has a much lower time complexity. According to experimental results, HSMOGA outperforms other nine state-of-art feature selection algorithms including five classic and four more recent algorithms in terms of kappa coefficient, accuracy, and G-mean for the data sets tested.

关键词： Feature selection Feature interaction Hybrid filter Symmetrical complementary coefficient multi-objective genetic algorithm

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MOSAT: Finding Safety Violations of Autonomous Driving Systems using multi-objective genetic algorithm 2022

MOSAT: Finding Safety Violations of Autonomous Driving Syste...

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30th ACM Joint European Software Engineering Conference / Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Tian, Haoxiang Jiang, Yan Wu, Guoquan Yan, Jiren Wei, Jun Chen, Wei Li, Shuo Ye, Dan Univ Chinese Acad Sci State Key Lab Comp Sci ISCAS Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Joint Lab Cyberspace Secur China Southern Power Grid Beijing Peoples R China Univ Chinese Acad Sci Chongqing Sch Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China

ISBN: (纸本)9781450394130

Autonomous Driving Systems (ADSs) are safety-critical systems, and safety violations of Autonomous Vehicles (AVs) in real traffic will cause huge losses. Therefore, ADSs must be fully tested before deployed on real world roads. Simulation testing is essential to find safety violations of ADS. This paper proposes MOSAT, a multi-objective search-based testing framework, which constructs diverse and adversarial driving environment to expose safety violations of ADSs. Specifically, based on atomic driving maneuvers, MOSAT introduces motif pattern, which describes a sequence of maneuvers that can challenge ADS effectively. MOSAT constructs test scenarios by atomic maneuvers and motif patterns, and uses multi-objective genetic algorithm to search for adversarial and diverse test scenarios. Moreover, in order to test the performance of ADS comprehensively during long-mile driving, we design a novel continuous simulation testing technique, which runs the scenarios generated by multiple parallel search processes alternately in the simulator and can continuously create different perturbations to ADS. We demonstrate MOSAT on an industrial-grade platform, Baidu Apollo, and the experimental results show that MOSAT can effectively generate safety-critical scenarios to crash ADSs and it exposes 11 distinct types of safety violations in a short period of time. It also outperforms state-of-the-art techniques by finding more 6 distinct safety violations on the same road.

关键词： Autonomous Driving System Safety Violation multi-objective genetic algorithm

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Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with multi-objective genetic algorithm for Sediment Flux Prediction during Typhoons

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WATER RESOURCES MANAGEMENT 2021年第8期35卷 2387-2408页

作者： Jhong, Bing-Chen Fang, Hsi-Ting Huang, Cheng-Chia Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 106335 Taiwan Taiwan Integrated Disaster Prevent Technol Engn C New Taipei 236654 Taiwan Natl Taipei Univ Business Ctr Gen Educ Taipei 100026 Taiwan

Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on sluice gates at a dam in a reservoir watershed. To address this issue, an assessment framework based on a core concept of Data-Information-Knowledge-Wisdom (DIKW) hierarchy is proposed in this study. First, for the reasonable training of the coupled method, a two-dimentional layer-averaged density current model, SRH2D, is applied to simulate reasonable SSC data. The limited SSC data at monitoring sites collected from the field and at dam face, inflow, and outflow discharges are collected for validation of a calibrated numerical model. Second, a well-known data-driven method, Support Vector Machine (SVM), is coupled with multi-objective genetic algorithm (MOGA) as a sediment-flux-prediction (SFP) model in the proposed framework to evaluate effective monitoring sites with SSC. An application in the Shih-Men Reservoir is implemented to demonstrate the contribution of the proposed investigation framework. The results indicate that the spatial turbidity current movement is reasonably simulated by the numerical model and appropriate as reliable data for the SFP model. The SSCs at measured points located on the lower level at dam face are significantly higher. Moreover, the results also show that the simulated SSC at the monitoring sites located near the inflow point and dam face are relatively useful for SFP. The analyzed results are concluded that the well-established observation equipment at the inflow point and near the dam is necessary for obtaining high-quality measured data, which has become a significant key issue on reservoir operation management (ROM). Also, the proposed framework is expected to be helpful to improve the benefit of ROM as reference for decision makers.

关键词： Sediment flux prediction Suspended sediment concentration Reservoir Support vector machine multi-objective genetic algorithm Typhoon

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Credit portfolio optimization: A multi-objective genetic algorithm approach

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BORSA ISTANBUL REVIEW 2022年第1期22卷 69-76页

作者： Wang, Zhi Zhang, Xuan Zhang, ZheKai Sheng, Dachen Beijing Univ Technol Int Business Sch Gengdan Inst Beijing Peoples R China Nanjing Audit Univ Inst Econ & Finance Nanjing Peoples R China

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets. Copyright (C) 2021, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V.

关键词： Credit portfolio optimization Heuristic optimization multi-objective genetic algorithm

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Zonal control for selective catalytic reduction system using a model-based multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2021年第3期22卷 911-920页

作者： Wang, Guoyang Qi, Jinzhu Liu, Shiyu Li, Yanfei Shuai, Shijin Wang, Zhiming Shandong Univ Sch Energy & Power Engn Jinan Shandong Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

It is challenging for aqueous urea injection control to achieve high NOconversion efficiency while restricting tailpipe ammonia (NH3) slip. Optimizing the selective catalytic reduction systems can reduce diesel engine emissions, potentially improve fuel economy and urea utilization efficiency, and finally reduce aftertreatment costs. In this article, a model-based multi-objective genetic algorithm is adopted to optimize selective catalytic reduction systems related to trade-off between NOemission and NH3 slip. Selective catalytic reduction model is a one-state selective catalytic reduction model based on continuous stirred tank reactor theory, which significantly reduces the computational burden. The optimal NH3 coverage ratio map was obtained globally based on world harmonized transient cycle. The effect of temperature on optimal NH3 coverage ratio, Zonal control logics extracted from the optimal solution, and the control problems on different zones were analyzed. The zonal control logics were validated on multiple test cycle with different initial NH3 coverage ratios. Results show that the zonal control achieves high NOconversion while restricting the tailpipe NH3 slip. With this method, NOemission and NH3 slip of optimal solution can meet the requirements of the Euro VI emission regulation for heavy-duty diesel engines.

关键词： Heavy-duty diesel engine NO emission control selective catalytic reduction ammonia coverage ratio optimization multi-objective genetic algorithm

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Enhancement of wind energy resources assessment using multi-objective genetic algorithm: A case study at Gabal Al-Zayt wind farm in Egypt

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2021年第14期18卷 1497-1509页

作者： Shaltout, Mohamed L. Mostafa, Moaz A. Metwalli, Sayed M. Cairo Univ Fac Engn Mech Design & Prod Engn Dept Giza 12613 Egypt

Estimation of wind speed distribution is essential for wind energy resources assessment, design of wind farms, and selection of suitable wind turbines. Two-parameter Weibull distribution function is widely used worldwide for wind energy resources assessment. As a case study, 1one-year field measurements at Gabal Al-Zayt wind farm in Egypt are used to estimate the Weibull parameters and to accurately assess the wind energy resource. In this work, seven statistical methods are adopted to estimate the Weibull parameters and their estimation accuracy is compared based on some common estimation errors. However, the improvement in one estimation error does not necessarily improve other types of errors. Consequently, a multi-objective genetic algorithm (MOGA) is adopted to investigate the tradeoffs among the competing estimation errors and to enhance the assessment of wind energy resources. The results show significant improvement in the estimation accuracy of the Weibull parameters using MOGA as compared to conventional statistical estimation methods. On the other hand, the case study at Gabal Al-Zayt wind farm reveals that the selection of wind turbines does not depend only on wind characteristics of the site but also on its environmental characteristics.

关键词： Wind energy assessment Gabal Al-Zayt multi-objective genetic algorithm Weibull distribution Statistical estimation

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