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Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

作者： Xin Ning Tianji Shi Shaoru Shang Zhihua Zhu Mengyao Qin School of Mechanical and Electrical Engineering Henan Institute of Science and Technology China

ISBN: (纸本)9798400716362

To further improve the structural performance of the 32/5t-10.5m double girder bridge crane, the optimal size design of the main beam is sought. The main beam of the bridge crane is taken as the optimization object, and stress analysis is conducted on multiple load cases to identify critical points. The goal is to achieve the best structural quality of the bridge crane while ensuring its stiffness performance. Topology optimization design is performed using a multi-objective genetic algorithm and response surface analysis is employed to optimize the thickness of the left and right web plates of the main beam. By comparing the stress analysis results before and after the reconstruction of the main beam, it is found that the reconstructed main beam has a 19.8% reduction in weight while meeting the strength and stiffness requirements. This achieves the objective of simplifying the structure, reducing weight, and lowering manufacturing costs.

关键词： Bridge Crane Main Beam Finite Element Analysis multi-objective genetic algorithm Optimization Design

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A Knee-Point Based NSAG-Ⅱ for Urban Tourism Path Planning

A Knee-Point Based NSAG-Ⅱ for Urban Tourism Path Planning

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2016战略管理国际会议

作者： Zhang Zhenzhong Ge Peng Ren Peiyu Zheng Weimin Liao Zhixue Business school Sichuan University

With the rapid development of tourism, people begin to pursue personalized and experience-oriented travelling ways. These new demands can be satisfied by the tourist route optimization with tourist preference, which is also an important way to improve tourist satisfaction and increase the competitiveness of scenic spots. Therefore how to provide tourist an optimum route is an important concern both for scenic manager and tourists. In order to provide the best tourism route for tourists, this paper firstly builds a multi-objective optimization model which takes minimum cost and maximum revenue as two objectives of this model. Furthermore, a knee-point based NSAG-Ⅱ was designed as solution methods.

关键词： tourism route multi-objective optimization model multi-objective genetic algorithm tourist preference

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Contractor Selection in Gas Well-drilling Projects with Quality Evaluation Using Neuro-fuzzy Networks

Contractor Selection in Gas Well-drilling Projects with Qual...

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International Conference on Future Information Engineering(FIE 2014)

作者： Roya M.Ahari S.T.A.Niaki Dept. of Industrial Engineerin Sharif University of Technology

Contractor selection for a project is an important decision, one for the project time and cost, next for the quality obtained by the project. Although the project managers can easily determine the project time and cost, the quality is usually undefined especially for un-experienced managers. With a learnable property, an approach is first introduced in this paper to quantify the quality obtained for a gas well drilling project. Then, based on these three objectives（time, cost, and quality）, a contractor selection problem is converted to an optimization problem. Next, the NSGA-II algorithm is utilized for solution. At the end, a sensitivity analysis is performed to select the parameters of the algorithm.

关键词： Contractor selection Quality evaluation multi-objective optimization Gas well drilling project multi-objective genetic algorithm

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A system-of-systems meta-architecting approach for seru production system design

A system-of-systems meta-architecting approach for seru prod...

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IEEE International Conference on System of Systems Engineering (SoSE)

作者： Samuel Vanfossan Cihan H. Dagli Benjamin Kwasa Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla USA

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180519

Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.

关键词： Seru production system meta-architecture generation multi-objective genetic algorithm fuzzy inference system system-of-systems architecting fuzzy inference system Product Design component assemble multi-objective evolutionary algorithm Assembly Lines Production systems

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A Radial Basis Function Classifier for the Automatic Diagnosis of Cerebral Vascular Accidents

A Radial Basis Function Classifier for the Automatic Diagnos...

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Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges Conference

作者： M.G. Ruano E. Hajimani A. E. Ruano Faculty of Science and Technology University of Algarve Faro Portugal and CISUC University of Coimbra Portugal Faculty of Science and Technology University of Algarve Faro Portugal Faculty of Science and Technology University of Algarve Faro Portugal and IDMEC Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781509024872

A Radial Basis Function Neural Network (RBFNN) based diagnosis system for automatic identification of Cerebral Vascular Accident (CVA) through analysis of Computer Tomographic images (CT) is presented. For the design of a neural network classifier, most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at their hand. Moreover, considering the domain of lesion detection from brain tissues, their feature space rarely contains symmetry/asymmetry information with respect to ideal mid-sagittal line. Another issue is how to handle multiple conflicting objectives in the design process, such as the maximization of both specificity and sensitivity, enforcing as well generalization. To deal with these challenges, a multi objective genetic algorithm (MOGA) based approach is used to determine the architecture of the classifier, its corresponding parameters and input features subject to multiple objectives, as well as their corresponding restrictions and priorities.

关键词： Cerebral Vascular Accident Intelligent support systems multi-objective genetic algorithm Neural Networks Symmetry Features

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multi-objective META LEVEL SOFTCOMPUTING BASED EVOLUTIONARY DESIGN OF LARGE-SCALESTRUCTURES

MULTI-OBJECTIVE META LEVEL SOFTCOMPUTING BASED EVOLUTIONARY ...

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第11届国际模糊系统协会世界大会

作者： Mohammad-R.Akbarzadeh-T Amir-R.Khorsand Department of Biomedical Engineering Azad University of MashhadIran Department of Electrical EngineeringFerdowsi University of Mashhad Department of Mechanical Engineering Ferdowsi University of MashhadIran

＜正＞ Evolutionary design of large-scale structures has been the topic of much recent research;however, such designs are usually hampered by the time consuming stage of prototype evaluations using standard finite element analysis （FEA）. In this paper, a multi-objective metal-level （MOML） soft computing based evolutionary scheme is proposed. The neural network （NN） learns to approximate the fitness function in order to substitute the time consuming large-scale problem analysis （L-SPA） by FEA, the fuzzy controller updates parameters of a genetic algorithm （GA） in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane whig. Then, robustness of the resulting optimization algorithm is tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a Fuzzy-GA-NN, a GA-NN and a simple GA in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms and applied to other benchmark problems. Specifically, the proposed approach finds better hardware designs more consistently while being computationally more efficient. Furthermore, these improvements are more pronounced as scale of the hardware design problem grows.

关键词： Structure Design Evolutionary Hardware multi-objective genetic algorithm Neural Network Large-Scale Problem Analysis Fuzzy Logic.

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A multi-drive Data Mining algorithm Based on Classification Method and Its Application in Investment Decision

A Multi-drive Data Mining Algorithm Based on Classification ...

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第七届模糊信息与工程国际会议(ICFIE'2014)、第一届运筹与管理国际会议 (ICORG'2014)暨中国运筹学会模糊信息与工程分会第七届年会、广东省运筹学会第二届年会、粤港澳运筹学会首届年会

作者： Yubin Zhong Li Tao Jialiang Xuan Yifan Zhang College of Mathematics and Information Science Guangzhou University College of Civil Engineering Guangzhou University UQ Business School the University of Queensland

This paper discusses the development of strategies in big data context,proposing a multi-drive data mining algorithm based on classification method and solving the investment decision problem when educational charity Goodgrant is planning to invest in 7900 colleges in the U.S. This paper first classifies all the colleges into to-be funded and to-be eliminated based on cluster analysis. Then, run the Fisher discriminant function parameters of the to-be funded colleges. After determining 52 indices which can influence investment return, select 39 effective indices according to Bayesian discriminant and calculate index weights. In the end,build a multi-objective optimal model based on the maximum of Fisher discriminant function and investment return, apply multi-objective genetic algorithm to compute the optimal investment weights of all the colleges and produce investment priority ranking according to the investment weight. The method in this paper can also be used in other investment area among big data context.

关键词： investment decision cluster analysis Bayesian discriminant Fisher discriminant multi-objective genetic algorithm

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Analysis and Research of Mooring System

Analysis and Research of Mooring System

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Jiahui Fan School of environment North China Electric Power University

ISBN: (纸本)9781510845008

In this paper, the original mooring system is optimized and improved under the condition of considering the water flow and water depth. The whole system is divided into a buoy system and a mooring system. For the buoy system, the moment of gravity of the buoy at the mean sea level is analyzed. For mooring system, the anchor cable will appear elastic deformation under the action of water flow force. So the particle spring model is constructed to analyze the force of the catenary segment. Then the new wind speed critical value is obtained. Thus, the state of existence of the chain is determined. A general optimization index is set up. And the optimal solution of the optimization index is calculated according to the multi-objective genetic algorithm. Thus the design of mooring system is completed.

关键词： Mooring system analytic hierarchy process multi-objective genetic algorithm

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Search for phosphors for use in displays and lighting using heuristics-based combinatorial materials science

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JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY 2009年第12期17卷 1073-1080页

作者： Sharma, Asish Kumar Sohn, Kee-Sun Sunchon Natl Univ Dept Mat Sci & Met Engn Sunchon City 540742 Chonnam South Korea

According to the recent demand for materials for use in various displays and solid-state lighting, new phosphors with improved performance have been consistently pursued. multi-objective genetic-algorithm-assisted combinatorial-material-search (MOGACMS) strategies have been applied to various multi-compositional inorganic systems to search for new phosphors and to optimize the properties of phosphors. In addition, the troublesome, complex problem of high-throughput experimentation (HTE), the inconsistency, which is frequently faced by combinatorial material scientists, is especially emphasized. The luminance and inconsistency was treated as two objective functions in our MOGACMS strategy to pinpoint and optimize promising phosphors with high photoluminance and reliable reproducibility. Using MOGACMS, several multi-dimensional oxide systems were screened in term of the minimization of inconsistency and the maximization of luminance.

关键词： Combinatorial material science multi-objective genetic algorithm PDP red phosphor

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HIV–TB co-infection treatment control using multi-objective optimized sliding mode

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Informatics in Medicine Unlocked 2020年 19卷

作者： Lakmesari, S. Hadipour Mahmoodabadi, M.J. Hadipour, S. Department of Mechanical Engineering Sirjan University of Technology Sirjan Iran Department of Infectious Diseases School of Medicine Guilan University of Medical Sciences Rasht Iran

Mycobacterium tuberculosis (MTB) and Human immunodeficiency virus (HIV) are two causes of infection which threaten human health over recent decades. There is abundant evidence indicating that these diseases are related to each other. People living with HIV have a 30-fold increase in tuberculosis infection compared to healthy people. In recent years, medical researchers and other specialists including engineers and basic scientists have tried to propose treatments for dangerous diseases by means of mathematical simulation. In this paper, a mathematical model of the HIV-TB co-infection individuals is regarded in order to represent different characteristics of such people. In the next step, a sliding mode controller is implemented on first and second HIV treatment rates to minimize the Total Burden resulted from the co-infection. Moreover, the multi-objective genetic algorithm (MOGA) is used to optimize the sliding mode controller coefficients. The MOGA is accurate and resilient and its Pareto front has a reasonable variety. In this method, designers acceptably use defeated points with more than one objective function and are also able to choose appropriate solutions according to their preferences. The results suggest that disease deaths, new infections, and immune reconstitution inflammatory syndrome cases play an important role in the problem. © 2020 The Authors

关键词： HIV-TB co-Infection Human immunodeficiency virus multi-objective genetic algorithm Sliding mode control Tuberculosis

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