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International Symposium on Software Reliability Engineering (ISSRE)

作者： Jon Ayerdi Aitor Arrieta Ernest Bota Pobee Maite Arratibel Mondragon Unibertsitatea Orona

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. However, this approach requires multiple test executions. In applications like Cyber-Physical Systems (CPSs), where the test executions can be very expensive in terms of time and resources needed, this can supose a problem. Therefore, it is paramount to optimize the test suite to reduce the costs of verifying the system. Test case selection is an optimization technique which accomplishes this by selecting a subset of test cases while aiming to preserve the effectiveness of the original test suite as much as possible. While there are many approaches for test case selection in the existing literature, none of them has been proposed for the metamorphic test case selection problem, where each metamorphic test case consists of a source and, at least, a follow-up test case pair. In this work, we present an evolutionary multi-objective approach for the metamorphic test case selection problem, adapting existing multi-objective test selection techniques and proposing new evolutionary operators and objective functions. Further-more, we evaluate our approach with a set of metamorphic tests developed for an industrial case study from the elevation domain. The results suggest that our approach outperforms both Random Search and the same metaheuristic algorithm without the new evolutionary operators we propose.

关键词： Measurement Costs Metaheuristics Cyber-physical systems Linear programming Search problems Software reliability

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Simulation-driven optimization of high-order meshes in ALE hydrodynamics

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COMPUTERS & FLUIDS 2020年 208卷 104602-104602页

作者： Dobrev, Veselin Knupp, Patrick Kolev, Tzanio Mittal, Ketan Rieben, Robert Tomov, Vladimir Lawrence Livermore Natl Lab 7000 East Ave Livermore CA 94550 USA Dihedral LLC Bozeman MT 59715 USA

In this paper we propose tools for high-order mesh optimization and demonstrate their benefits in the context of multi-material Arbitrary Lagrangian-Eulerian (ALE) compressible shock hydrodynamic applications. The mesh optimization process is driven by information provided by the simulation which uses the optimized mesh, such as shock positions, material regions, known error estimates, etc. These simulation features are usually represented discretely, for instance, as finite element functions on the Lagrangian mesh. The discrete nature of the input is critical for the practical applicability of the algorithms we propose and distinguishes this work from approaches that strictly require analytical information. Our methods are based on node movement through a high-order extension of the Target-Matrix optimization Paradigm (TMOP) of [1]. The proposed formulation is fully algebraic and relies only on local Jacobian matrices, so it is applicable to all types of mesh elements, in 2D and 3D, and any order of the mesh. We discuss the notions of constructing adaptive target matrices and obtaining their derivatives, reconstructing discrete data in intermediate meshes, node limiting that enables improvement of global mesh quality while preserving space-dependent local mesh features, and appropriate normalization of the objective function. The adaptivity methods are combined with automatic ALE triggers that can provide robustness of the mesh evolution and avoid excessive remap procedures. The benefits of the new high-order TMOP technology are illustrated on several simulations performed in the high-order ALE application BLAST [2]. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： TMOP Mesh optimization ALE hydrodynamics r-adaptivity High-order finite elements

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Data-Driven multiobjective Controller optimization for a Magnetically Levitated Nanopositioning System

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2020年第4期25卷 1961-1970页

作者： Li, Xiaocong Zhu, Haiyue Ma, Jun Teo, Tat Joo Teo, Chek Sing Tomizuka, Masayoshi Lee, Tong Heng Singapore Inst Mfg Technol Mechatron Grp Singapore 138634 Singapore Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore

The performance achieved with traditional model-based control system design approaches typically relies heavily on accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task;and the usually necessary practical approximations often renders the automation system to operate in a nonoptimal condition. This problem can be greatly aggravated in the case of a multiaxis magnetically levitated (maglev) nanopositioning system where the fully floating behavior and multiaxis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under nonoptimal conditions. These motion data essentially contain rich information;therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate toward optimality in a data-driven manner. Along this line then, this article proposes a data-driven model-free controller optimization approach that learns from the past nonoptimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multiobjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data;the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In this article, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

关键词： Magnetic levitation Nanopositioning Force Motion control Cost function Robots Data-driven optimization iterative feedback tuning (IFT) learning-based control magnetic levitation nanopositioning precision motion control robot control robot learning

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Advanced Computational approaches in HR: Utilizing Neural Networks and Genetic Algorithms for Talent Acquisition

Advanced Computational Approaches in HR: Utilizing Neural Ne...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Rishabh Sharma B Reddy Centre for Research Impact & Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Despite the constant changes within today’s Human Resources (HR) field, talent acquisition stands out as one of the most important strategic processes determining the performance of a company. The conventional recruiting approach is mostly performed and oftentimes proves to be time-consuming and non-inclusive. To address these challenges, this research focuses on the combination of intelligent technologies, namely neural networks and genetic algorithms in the context of talent acquisition. As a result, it was decided to use neural networks that have the best function for processing and analyzing massive unstructured information, such as resumes and job descriptions, to determine candidate suitability. Hypothesis 3 posited that since GAs are about optimization, multi-objective and were used to consider various factors at one time in the context of candidate job matching. As data sources, resumes of the IT employees, job descriptions of the positions, and employee performance analytics of this leading global technology company were used for the study. The CNN model needs to be further trained using other candidate sets of questions and achieve a screening accuracy of 85%, and an AUC-ROC score of 0.88 to properly screen candidates. The genetic algorithm model was also quite good with a classification accuracy of 82% and by using AUC-ROC it scored well 0.85. The best results employing both NNs and GAs indicated the highest performance measures; accuracy equal to 87%, precision equal to 85%, recall equal to 86%, and an AUC-ROC of 0.90. Such findings support the effectiveness of using both neural networks and genetic algorithms in redefining talent management by automating the process and reducing errors and prejudice in choosing talents. The implications for practical application for HR professionals and organizations are immense and identify that the use of these more complex computational methodologies can indeed provide a step up in recruiting performance. The study a

关键词： Technological innovation Accuracy Computational modeling Soft sensors Neural networks Resumes Companies Transforms Predictive models Genetic algorithms

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A phase change material selection using the interval-valued target-based BWM-CoComultiMOORA approach: A case-study on interior building applications

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APPLIED SOFT COMPUTING 2020年 95卷 106508-106508页

作者： Maghsoodi, Abtin Ijadi Soudian, Shahrzad Martinez, Luis Herrera-Viedma, Enrique Zavadskas, Edmundas Kazimieras Univ Auckland Fac Business & Econ Business Sch Dept Informat Syst & Operat Management Auckland 1010 New Zealand Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada Univ Jaen Dept Comp Sci Campus Las Lagunillas S-N Jaen 23071 Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain Vilnius Gediminas Tech Univ Inst Sustainable Construct LT-10223 Vilnius Lithuania

The move towards sustainable development and energy efficient solutions in the built environment has led to develop innovative solutions, particularly in the area of indoor environmental comfort in buildings. Phase change materials (PCMs) are a potential approach to target building energy use reduction. PCMs are smart materials that have been commercialized and used in buildings for effective regulation of surface and indoor air temperature fluctuations and peak energy reductions. Appropriate PCM selection is the critical step in PCM system design that determines the full effectiveness and applicability of PCM integrated building applications. Optimal selection of PCMs can be effectively executed with the support of multiple attribute decision making (MADM) approaches. Although, the traditional MADM methods have usually focused on beneficial and non-beneficial attributes, in real-world and practical problems, decision-makers tend to determine the rank of an optimal alternative based on the target values of their attributes. In response to the knowledge gap of an existing practical and functional PCM selection solution, this study proposed a hybrid and novel target-based MADM approach that combines the best-worst method (BWM) with COmbined COmpromise SOlution (CoCoSo) and multi-objective optimization of ratio analysis plus the full multiplicative form (multiMOORA) with an interval-valued structure called the IV-T-BWM-CoComultiMOORA approach. A case study is examined to select the optimal PCM for interior building surface applications based on a case-specific construction project in Toronto, Canada. Two separate scenarios are considered, one based on thermophysical specifications and one based on managerial preferences. The connection between both technical and managerial criteria was demonstrated to clearly affect the decision-making process to take into account both thermophysical properties of PCM alternatives in the context of risk factors and design consideration

关键词： Phase change material (PCM) Material selection Interval numbers Best-worst method (BWM) Target-based COmbined COmpromise SOlution (T-coCoSo) Target-based multi-objective optimization of ratio analysis plus the full multiplicative form (T-multiMOORA)

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Thermal–structure coupling analysis and multi-objective optimization of motor rotor in MSPMSM

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Chinese Journal of Aeronautics 2019年第7期32卷 1733-1747页

作者： Luxin ZHAI Jinji SUN Xin MA Weitao HAN Xiaosan LUO School of Instrumentation Science&Opto-electronics Engineering Beihang UniversityBeijing100083China CRRC Qingdao Sifang Co. Ltd.Qingdao266111China

With the high speed, the rotor of magnetically suspended permanent magnet synchronous motor(MSPMSM) suffers great thermal stress and mechanical stress resulting from the temperature rise problem caused by rotor losses, which leads to instability and *** this paper, the mechanical–temperature field coupling analysis is conducted to analyze the relationship between the temperature field and structure, and multi-objective optimization of a rotor is performed to improve the design reliability and efficiency. Firstly, the temperature field is calculated by the 2 D finite element model of MSPMSM and the method of applying the 2 D temperature result to the 3 D finite element model of the motor rotor equivalently is proposed. Then the thermal–structure coupling analysis is processed through mathematic method and finite element method(FEM),in which the 3 D finite element model is established precisely in a way and approaches the practical operation state further. Moreover, the impact produced by the temperature and structure on the mechanical strength is analyzed in detail. Finally, the optimization mathematical model of the motor rotor is established with Sequential Quadratic Programming-NLPQL selected in the optimization scheme. Through optimization, the strength of the components in the motor rotor increases obviously and satisfies the design requirement, which to a great extend enhances the service life of the MSPMSM rotor.

关键词： FEM MSPMSM multi-objective optimization Thermal stress Thermal–structure coupling analysis

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An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

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ALGORITHMS 2020年第12期13卷 337-337页

作者： Kuhn, Mathias Voelker, Michael Schmidt, Thorsten Tech Univ Dresden Inst Mat Handling & Ind Engn D-01062 Dresden Germany

Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs.

关键词： simulation-based optimization stochastic project scheduling genetic algorithm discrete event simulation composite priority rules

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A Dynamic multi-Constraints Handling Strategy for multi-objective Energy Management of Microgrid Based on MOEA

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IEEE ACCESS 2019年 7卷 138732-138744页

作者： Li, Xin Xia, Re Wuhan Univ Technol Sch Energy & Power Engn Wuhan 430063 Hubei Peoples R China Wuhan Univ Sch Power & Mech Engn Dept Mech Engn Wuhan 430072 Hubei Peoples R China

multi-objective energy management of microgrid (MEM-MG) is to optimize the output power of micro-sources to satisfy the practical constraints through the whole optimization process while minimizing the operation costs and air pollutions of the MG system. In this paper, an MEM-MG optimization model is established and the practical constraints are described. Aiming at handling various types of constraints, a dynamic multi-constraints handling strategy (DMCS) is proposed based on multi-objective optimization evolutionary algorithm (MOEA), by which a potential solutions are evaluated from two aspects during the evolutionary process, namely the overall violations and the amount of the violated constraints. The comprehensive evaluation indexes are introduced to estimate the constraints violations of different dimensions as well as the number of the violated constraints. In addition, the common used Deb's constraints handling approach is modified into dynamic evaluation criteria, which can decrease the values of the two factors above simultaneously. Thereafter, DMCS is combined with non-dominated sorting genetic algorithm II (NSGAII) and applied to several MEM-MG optimization problems with different types of constraints. The simulation results are compared with those obtained by other constraints handling approaches. The case studies verify the effectiveness of the proposed DMCS in handling various types of constraints and finding optimal trade-off solutions for MEM-MG optimization problems.

关键词： Energy management microgird multi-constraints handling multi-objective optimization dynamic evaluation criteria

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An online variable-fidelity optimization approach for multi-objective design optimization

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STRUCTURAL AND multiDISCIPLINARY optimization 2019年第3期60卷 1059-1077页

作者： Shu, Leshi Jiang, Ping Zhou, Qi Xie, Tingli Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China

multi-objective genetic algorithms (MOGAs) are effective ways for obtaining Pareto solutions of multi-objective design optimization problems. However, the high computational cost of MOGAs limits their applications to practical engineering optimization problems involving computational expensive simulations. To address this issue, a novel variable-fidelity (VF) optimization approach for multi-objective design optimization is proposed, in which a VF metamodel is embedded in the computation process of MOGA to replace the expensive simulation model. The VF metamodel is updated in the optimization process of MOGA, considering the cost of simulation models with different fidelity and the influence of the VF metamodel uncertainty. A normalized distance constraint is introduced to avoid selecting clustered sample points. Four numerical examples and two engineering cases are used to demonstrate the applicability and efficiency of the proposed approach. The results show that the proposed approach can obtain Pareto solutions with good quality and outperforms the other four approaches considered here as references in terms of computational efficiency.

关键词： multi-objective optimization Variable-fidelity optimization multi-objective genetic algorithms Metamodel uncertainty

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A practical pretreatment planning method of multiple puncturing for thermal ablation surgery

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2020年第4期40卷 1469-1485页

作者： Li, Jing Xu, Yuanqi Shen, Nanyan Feng, Lanyun Ran, Zhuang Deng, Zongqian Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Intelligent Mfg & Robot Shanghai 200444 Peoples R China Fudan Univ Shanghai Canc Ctr Dept Integrat Oncol Shanghai Peoples R China Fudan Univ Shanghai Med Coll Dept Oncol Shanghai Peoples R China

Thermal ablation surgery serves as one of the main approaches to treat liver tumors. The pretreatment planning, which highly demands the experience and ability of the physician, plays a vital role in thermal ablation surgery. The planning of multiple puncturing is necessary for avoiding the possible interference, destroying the tumor thoroughly and minimizing the damage to healthy tissue. A GPU-independent pretreatment planning method is proposed based on multi-objective optimization, which takes the most comprehensive constraints into consideration. An adaptive decision method of closing kernel size based on Jenks Natural Breaks is utilized to describe the final feasible region more accurately. It should be noted that the reasonable procedure of solving the feasible region and the use of KD tree based high dimensional search approach are used to enhance the computational efficiency. Seven constraints are handled within 7 s without GPU acceleration. The Pareto front points of nine puncturing tests are obtained in 5 s by using the NSGA-II algorithm. To evaluate the maximum difference and similarity between the planning results and the puncturing points recommended by the physician, Hausdorff distance and overlap rate are respectively developed, the Hausdorff distances are within 30 mm in seven out of nine tests and the average value of overlap rate is 73.0% for all the tests. The puncturing paths of high safety and clinical-practice compliance can be provided by the proposed method, based on which the pretreatment planning software developed can apply to the interns' training and ability evaluating for thermal ablation surgery. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Image preprocessing Liver tumor multi-objective optimization NSGA-II Puncturing path

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