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

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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Method research on equilibrium cycle reloading pattern optimization for the HPR1000 reactor core using deep learning-enhanced multi-objective genetic algorithm

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ANNALS OF NUCLEAR ENERGY 2025年 220卷

作者： Butt, Muhammad Kamran Wan, Chenghui Wei, Luo Khan, Izat Cao, Liangzhi Xi An Jiao Tong Univ Sch Nucl Sci & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

A novel methodology for designing and optimizing a PWR's equilibrium cycle reloading pattern using deep learning and a multi-objective genetic algorithm (MOGA) has been developed. The deep-learning model efficiently and accurately predicted reactor physics parameters, particularly fuel assembly burnups at the end of the cycle (EOC), and formed a fitness function. The fitness function takes the absolute difference between the conformable fuel assemblies' burnups at the beginning of the cycle (BOC) and the EOC, which narrows down the potential equilibrium reloading patterns. The deep-learning model was coupled with MOGA, which simultaneously optimized multiple objectives for the design and optimization of an equilibrium cycle. Applied to the HPR1000 reactor, the method achieved the first equilibrium cycle length of 473.1 Effective Full Power Days (EFPDs) and an average of 471.1 EFPDs over ten cycles, meeting all the parameters of reactor safety design criteria.

关键词： Deep-learning algorithm multi-objective genetic algorithm Equilibrium-cycle optimization HPR1000

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Research on the Optimization Design of High-Rise Office Building Performance Based on a multi-objective genetic algorithm

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BUILDINGS 2025年第10期15卷 1636-1636页

作者： Yuan, Zhaohui Pan, Jiajie Chen, Xing Peng, Yiyan Hunan Univ Sch Architecture & Planning Changsha 410006 Peoples R China Hefei Univ Technol Sch Architecture & Art Hefei 230002 Peoples R China

Office buildings often consume a large amount of energy during their operational phase, primarily due to insufficient consideration of the coordination among energy consumption, thermal comfort, and visual comfort in the design process. This study employs a multi-objective genetic algorithm to optimize the overall performance of office buildings by parameterizing seven key design variables: floor plan aspect ratio, building orientation angle, window-to-wall ratios (WWRs) in all directions, shading strategy, shading device orientation, shading device length, and shading device spacing. A building performance simulation model was established to conduct a global optimization search, with simultaneous analysis across the east, south, west, and north fa & ccedil;ades to obtain a set of Pareto-optimal solutions that satisfy multiple performance objectives. The results indicate that optimal comprehensive performance across energy use, thermal comfort, and visual comfort can be achieved under the following conditions: a floor plan aspect ratio of 0.67-1, building rotation of 0-20 degrees clockwise, an east-facing WWR of 0.4, south- and west-facing WWRs of 0.2-0.4, and a north-facing WWR of 0.4-0.6. For shading, horizontal devices with a length of 0.8-1.0 m, downward tilt angle of 10-30 degrees, and spacing of 0.6-1.2 m are recommended. These findings provide scientific parameter references and optimization pathways for the design of high-performance office buildings in various climate conditions.

关键词： building performance optimization visual comfort thermal comfort energy consumption office buildings 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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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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A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Zhuang, Mengzhen Zhang, Wei Tang, Hongtao Li, Xinyu Wang, Kaipu Wuhan Univ Technol Wuhan Sch Mech & Elect Engn Wuhan 430074 Peoples R China Hubei Prov Engn Res Ctr Robot & Intelligent Mfg Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

关键词： Green shop scheduling multi-objective genetic algorithm Reinforcement learning Machine speed Parameters adjusting strategy

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Design Optimization of an Iron Core for Cup-Type AMF Contacts Using genetic Aggregation Response Surface and multi-objective genetic algorithm

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Huang, Lei Ma, Hui Wang, Zihan Li, Yuanzhao Geng, Yingsan Wang, Jianhua Liu, Zhiyuan Xian Jiao tong Univ XJTU Dept Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

For cup-type AMF contacts (CACs) in high voltage vacuum interrupters (VIs), the iron core is crucial for enhancing the interrupting capability. The objective of this paper is to employ the genetic Aggregation response surface and multi-objective genetic algorithm for optimizing the AMF characteristics in CACs, which could make the design approach more efficient. Three objectives were chosen for optimization: the highest AMF flux density measured at the central plane within the contact gap (B-AMF), the corresponding phase shift time for B-AMF (t(s)) and the weight of the iron core (m). Finally, an optimal iron core structure was suggested. When comparing the original structure with the optimal one, the AMF in the optimal structure was enhanced across 64.0% of the intermediate plane. Particularly in the central area, the AMF increased by 10.6%. These results demonstrate that significant enhancements were observed in the AMF characteristics of the optimal structure, essential for improving the interrupting capacity of VIs.

关键词： Iron Optimization Response surface methodology Magnetic cores Magnetic fields genetic algorithms Correlation Axial magnetic field cup-type AMF contact design optimization iron core multi-objective genetic algorithm response surface

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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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Inverse design of dual-bandpass metasurface filters empowered by the multi-objective genetic algorithm

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OPTICS COMMUNICATIONS 2024年 566卷

作者： Yu, Ke Ge, Jiahao Li, Haonan Zhang, Yaqiang Dong, Hongxing Zhang, Long Shanghai Univ Sch Microelect Shanghai 201800 Peoples R China Chinese Acad Sci Shanghai Inst Opt & Fine Mech Key Lab Mat High Power Laser Shanghai 201800 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Hangzhou Inst Opt & Fine Mech Hangzhou 311421 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou 310024 Peoples R China

multi-band metasurface filters are becoming increasingly pivotal in high-capacity communication technologies. Traditional methods for designing metasurface structures, to date, have relied on empirical approaches to obtain target electromagnetic responses, thereby suffering from low efficiency. Here, we demonstrate an advanced approach for the inverse design of a multi-band metasurface filter, which consists of a multi-objective genetic algorithm (MOGA) in tandem with equivalent circuit model (ECM) analysis. This integration converts specific frequency response requirements into ECM parameter constraints, significantly streamlining the metasurface design and optimization process and offering superior solutions. Using this inverse design method, we theoretically propose a dual-bandpass metasurface filter which can exhibit transmission passbands in any two adjacent frequency ranges among the X-, Ku-, and K-bands. Further, numerical simulations validate the performances of the proposed device, which show great agreement with MOGA-based predictions. Our results pave the way to the effective inverse design of multi-passband metasurface filters which are useful in many applications, such as microwave filters, radar and satellite communication systems, and radio frequency identification devices.

关键词： Metasurface Bandpass filters multi-objective genetic algorithm Equivalent circuit model

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