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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Yin, Xiaoqing Yi, Hao Yang, Zebin Zhuo, Fang Zhang, Yao Wei, Wei Xi'An Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipmen Xi'an China

ISBN: (纸本)9798350328813

With the development of high-proportion renewable energy systems, the number of distributed nonlinear loads has been increasing, which causes a growing harmonic problem in the distribution network. In this situation, the traditional point-to-point harmonic compensation method is inefficient, since it needs to configure plenty of control equipment. This article presents a multi-objective optimal location algorithm of multiple shunt active power filters (SAPFs), considering that some harmonic sources are unknown in the distribution network. Firstly, based on the measurement equations, the least-square method is used to estimate the unmeasured power quality data, so as to improve the accuracy of the multi-bus harmonic mitigation model. Secondly, a multi-objective genetic algorithm is proposed for determining the Pareto-optimal set of the equipment layout to suppress multi-bus harmonic distortion. Finally, a standard IEEE-18 buses simulation model with 6 nonlinear loads is built to verify the proposed optimal location strategy. The simulation results show that the proposed algorithm can ensure that harmonic distortion indexes of each bus meet the recommended standards with few devices and total capacity. © 2023 IEEE.

关键词： harmonic comprehensive suppression harmonic state estimation multi-objective genetic algorithm optimal configuration

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Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期158卷 106799-106799页

作者： Reis, Diogo R. Santos, Bruno C. Bleicher, Lucas Zarate, Luis E. Nobre, Cristiane N. Pontifical Catholic Univ Minas Gerais PUC Minas 500 Dom Jose Gaspar St Bldg 20 BR-30535901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Ave Pres Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model's quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes' classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes's characteristics. The datasets and implementations are openly available.

关键词： Bioinformatics Proteins Enzyme class prediction Support vector machine Dimensionality reduction Feature selection multi-objective genetic algorithm

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Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 91-110页

作者： Wenzheng Liu Heming Zhang Chao Tang Shuangfei Wu Hongguang Zhu National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China Gridsum Holding Inc.Beijing 100086P.R.China Center of Intelligent Control and Telescience Tsinghua Shenzhen International Graduate School Shenzhen 518055 P.R.China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing 100084 P.R.China

Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of ***-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of *** composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application *** this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel ***,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse *** properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are *** on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are *** show that the effectiveness of the model integration method proposed in this paper is verified.

关键词： CPS model reuse model composition co-simulation distance multi-objective genetic algorithm

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Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年第PartB期302卷 114073-114073页

作者： Ma, Changhui Xie, Yaowen Duan, Hanming Wang, Xiaoyun Bie, Qiang Guo, Zecheng He, Lei Qin, Wenhua Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Gansu Peoples R China China West Normal Univ Sch Geog Sci Nanchong 637002 Sichuan Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Gansu Peoples R China

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel;The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate t

关键词： Grassland utilization intensity Spatial quantification method Qinghai-Tibetan plateau Disturbance Artificial neural networks multi-objective genetic algorithm

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Prioritizing Natural Language Test Cases Based on Highly-Used Game Features 2023

Prioritizing Natural Language Test Cases Based on Highly-Use...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Viggiato, Markos Paas, Dale Bezemer, Cor-Paul Univ Alberta Edmonton AB Canada Prodigy Educ Oakville ON Canada

ISBN: (纸本)9798400703270

Software testing is still a manual activity in many industries, such as the gaming industry. But manually executing tests becomes impractical as the system grows and resources are restricted, mainly in a scenario with short release cycles. Test case prioritization is a commonly used technique to optimize the test execution. However, most prioritization approaches do not work for manual test cases as they require source code information or test execution history, which is often not available in a manual testing scenario. In this paper, we propose a prioritization approach for manual test cases written in natural language based on the tested application features (in particular, highly-used application features). Our approach consists of (1) identifying the tested features from natural language test cases (with zero-shot classification techniques) and (2) prioritizing test cases based on the features that they test. We leveraged the NSGA-II genetic algorithm for the multi-objective optimization of the test case ordering to maximize the coverage of highly-used features while minimizing the cumulative execution time. Our findings show that we can successfully identify the application features covered by test cases using an ensemble of pre-trained models with strong zero-shot capabilities (an F-score of 76.1%). Also, our prioritization approaches can find test case orderings that cover highly-used application features early in the test execution while keeping the time required to execute test cases short. QA engineers can use our approach to focus the test execution on test cases that cover features that are relevant to users.

关键词： Test case prioritization multi-objective genetic algorithm Zeroshot classification Feature usage

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Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi-criteria decision-making approach

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JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2023年第1-2期30卷 44-61页

作者： Sadhu, Tithli Lahiri, Sandip Kumar Roy, Jagannath Bhattacharjee, Ashish Chakrabarty, Jitamanyu Natl Inst Technol Durgapur Dept Chem Durgapur 713209 India SR Univ Sch Agr Dept Biochem Hanumakonda India Natl Inst Technol Durgapur Dept Chem Engn Durgapur West Bengal India Natl Inst Technol Warangal Dept Math Hanumakonda India Natl Inst Technol Durgapur Dept Biotechnol Durgapur India

The objective of the study was to assess the optimum frying condition of fish considering the multiple perspectives (retention of nutritional quality indices [NQI], reduction of preparation time, and improvement of health benefit) to satisfy consumer-preferred sensory attributes by controlling the most impactful process variables (temperature, time, and oil amount). The multi-criteria decision-making (MCDM) approach is appropriate to handle the numerous conflicting criteria and numerous multiple objectives. First, an artificial neural network (ANN) model was developed to build a non-linear correlation between the cooking process parameters and NQI by an automatic exhaustive search of all available algorithms and activation functions. All the NQI are conflicting in nature. Therefore, the ANN-based multi-objective genetic algorithm was implemented to obtain the Pareto optimal solutions to improve all NQI simultaneously. Five optimised conditions were selected amongst the Pareto optimal solutions, satisfying the above-mentioned multiple criteria. Finally, a well-known MCDM approach, the analytical hierarchy process (AHP), was applied for sensory analysis to evaluate the overall acceptance of the optimised conditions based on the relative importance of consumers' general sensory modalities (flavour, colour & appearance, and taste). Furthermore, the following condition (140.01 & DEG;C, 7.62 min, 47.87 ml oil/kg of fish) was selected as the most accepted in terms of all quality attributes that may be implemented as the standard condition in domestic and industrial purposes.

关键词： analytical hierarchy process artificial neural network frying multi-objective genetic algorithm nutritional quality indices sensory evaluation

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multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2012年第7期25卷 625-635页

作者： Arkat, Jamal Farahani, Mehdi Hosseinabadi Ahmadizar, Fardin Univ Kurdistan Dept Ind Engn Sanandaj 416 Iran

Integrated design of cellular manufacturing (CM) systems consist of three major decisions: cell formation (CF), cellular layout (CL) and planning issues such as cellular scheduling (CS). This article presents a mathematical model to concurrently identify the formation of cells, cellular layout and the operations sequence with the objective of minimising total transportation cost of parts as well as minimising makespan. A multi-objective genetic algorithm (MOGA) is then developed to solve the problem. The proposed MOGA exploits a novel evolutionary process which enables it to efficiently find Pareto optimal solutions. Computational results show the advantages of the proposed integrated approach and the superiority of the proposed MOGA over some well-known multi-objective evolutionary algorithms.

关键词： cell formation cellular layout operations scheduling multi-objective genetic algorithm

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A Novel Slotted Cylinder Spring Geometry with an Improved Energy Storing Capacity

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第12期47卷 15539-15549页

作者： Warzecha, Mariusz Michalczyk, Krzysztof Machniewicz, Tomasz AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland

Slotted cylinder springs combine some advantages of helical and disc springs. They do not twist when they are compressed and, at the same time, can carry axial and transverse loads. Their wider use is hampered by the uneven distribution of stresses during operation resulting from their geometry, which makes their load capacity relatively low. The paper presents an attempt to modify the geometry of a conventional slotted spring using the multi-objective genetic algorithm optimization. It exemplifies holistic application of various software tools aiding design process. The analyses confirmed a significant reduction of maximum stress at a given load when compared to the conventional design. The optimization allowed the energy storage capacity to be increased by more than two and a half times compared to the reference spring. Analyses showed that a 39% reduction in maximum equivalent stress was achieved for the same axial load. The experimental tests allowed the results obtained by numerical means to be verified.

关键词： Slotted spring Optimization multi-objective genetic algorithm Elastic element Spring design

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multi-response Optimization of Thermal Cycling Process for Al6092/SiC/ZrW2O8 Composites Using RSM-MOGA

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第6期47卷 7669-7682页

作者： Zhang, Shihao Hou, Qinglin Fu, Zhixiang Jiang, Haiyun Hunan Univ Technol Sch Packaging & Mat Engn Zhuzhou 412007 Peoples R China Hunan Univ Technol Natl & Local Joint Engn Res Ctr Adv Packaging Mat Zhuzhou 412007 Peoples R China

Thermal cycling can reduce or eliminate thermal stresses in aluminum matrix composites, which is essential to improve the dimensional stability of the composites. In this work, the effect of thermal cycling process on the thermophysical properties of 6092 aluminum alloy/silicon carbide/zirconium tungstate composites was investigated by combining response surface optimization and multi-objective genetic algorithm. The purpose is to elucidate the effects of the process of heating temperature, heating time and cycles on the coefficient of thermal expansion (CTE), micro-strain and thermal conductivity of the composites. A series of robust response models were proposed and verified using analysis of variance, externally studentized residuals distribution and the predicted residuals distribution. According to these models, the heating temperature, heating time and cycles interact in a nonlinear manner concerning on thermophysical properties. The experimental results confirmed that thermal cycling can effectively reduce or eliminate thermal residual stresses in the composites and induce the phase transformation process of zirconium tungstate. The heating temperature plays a dominant role in promoting the thermal mismatch stress release between the metal matrix and the reinforcing particles. The Pareto-optimal hypersurface results on the response surface were obtained according to the non-dominated sorting genetic algorithms (NSGA-II), indicating that the optimal thermal cycling processes were 200 degrees C, 2 h and 3 cycles with corresponding CTE, micro-strain and thermal conductivity of 20.55 x 10(-6)/K, 1.16 x 10(-3) and 116.68 W/(m K), respectively.

关键词： Thermal cycling Response surface methodology multi-objective genetic algorithm Thermophysical

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A multi-objective genetic algorithm for intelligent software project scheduling and team staffing

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2013年第1期7卷 59-80页

作者： Stylianou, Constantinos Andreou, Andreas S. Univ Cyprus Dept Comp Sci Nicosia Cyprus Cyprus Univ Technol Dept Comp Engn & Informat 31 Archbishop Kyprianou AvePOB 50329 CY-3036 Lemesos Cyprus

Allocation of human resources is considered one of the most important activities carried out by software project managers, since human resources are essentially the only type of resource utilized in software development. Part of human resource allocation involves the scheduling of tasks and the staffing of teams with suitable developers, which for project managers are activities that are often very difficult to carry out due to the large number of possible permutations and factors influencing selection. In addition, no standardized technique is available for software project managers that can be adopted to carry out these activities. Consequently, proper human resource allocation is now gradually being regarded as a critical factor that can influence software project success and can directly contribute to providing customers with software products on time, within budget and with the adequate level of quality. The aim of the research work, therefore, is to form an approach to help software project managers undertake the responsibility of scheduling projects and forming teams in the best possible way given a set of tasks and developers. The approach employs a multi-objective genetic algorithm to optimize various aspects of scheduling and staffing in the form of objective functions with respect to project duration and developer skills and at the same time handling constraints concerning task dependencies and assignment conflicts. The approach was assessed using a set of scenarios of varying project size and complexity that depict possible real-world software project instances. The results obtained show that the proposed approach is capable of providing feasible project schedules and team assignments for software projects with differing sizes and complexities, whereas its ability to provide optimal solutions is limited by the complexity of software projects. Software project managers do not always have the same goals and criteria when planning for projects. Therefore, t

关键词： Software project management project scheduling team staffing multi-objective genetic algorithm

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