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3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS)

作者： Wang, Yongji Zhao, Wenliang Yu, Mingjie Yang, Yubo Kwon, Byung-il Shandong Univ Sch Elect Engn Jinan Peoples R China Hanyang Univ Dept Elect Syst Engn Ansan South Korea

ISBN: (纸本)9781728156224

In this paper, a 24-slot/25-pol e dual-stator flux switching permanent magnet machine (DSFSPMM) is designed and optimized to improve electromagnetic performance by combining the spoke-type magnet configurations, phase-group concentrated coil windings and an unaligned arrangement of rotor teeth. The design concept and operation principle of the machine are introduced in detail. Due to the complex dualstator structure, the proposed DSFSPMM model is established and analyzed by using the finite element method software. To achieve the optimal structure size of the machine, the multiobjective genetic algorithm (MOGA) is carried out based on the single-parameter scanning. As a result, it shows that the optimized model exhibits high torque density, low torque ripple, and high efficiency.

关键词： finite element method flux switching permanent magnet machine multi-objective genetic algorithm

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Optimal position of rectangular vortex generator for heat transfer enhancement in a flat-plate channel

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ASME Summer Heat Transfer Conference

作者： Khan, Tariq Amin Li, Wei Zhang, Zhengjiang Du, Jincai Khan, Sadiq Amin Liu, Fu Zhejiang Univ Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Collaborat Innovat Ctr Adv Aeroengine Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Zhejiang Univ Dept Mat Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9780791857885

Heat transfer is a naturally occurring phenomenon which can be greatly enhanced by introducing longitudinal vortex generators (VGs). As the longitudinal vortices can potentially enhance heat transfer with small pressure loss penalty, VGs are widely used to enhance the heat transfer of flat-plate type heat exchangers. However, there are few researches which deal with its thermal optimization. Three dimensional numerical simulations are performed to study the effect of angle of attack and attach angle (angle between VG and wall) of vortex generator on the fluid flow and heat transfer characteristics of a flat-plate channel. The flow is assumed as steady state, incompressible and laminar within the range of studied Reynolds numbers (Re = 380, 760, 1140). In the present work, the average and local Nusselt number and pressure drop are investigated for Rectangular vortex generator (RVG) with varying angle of attack and attach angle. The numerical results indicate that the heat transfer and pressure drop increases with increasing the angle of attack to a certain range and then decreases with increasing angle of attack. Moreover, the attach angle also plays an importance role;a 90 attach angle is not necessary for enhancing the heat transfer. Usually, heat transfer enhancement is achieved at the expense of pressure drop penalty. To find the optimal position of vortex generator to obtain maximum heat transfer and minimum pressure drop, the data obtained from numerical simulations are used to train a BRANN (Bayesian-regularized artificial neural network). This in turn is used to drive multi-objective genetic algorithm (MOGA) to find the optimal parameters of VGs in the form of Pareto front. The optimal values of these parameters are finally presented.

关键词： Heat transfer enhancement longitudinal vortex generator flat-plate channel Artificial neural network multi-objective genetic algorithm

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Cost-effective retrofits of power grids based on critical cascading failure scenarios identified by multi-group non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2020年 49卷

作者： Choi, Eujeong Song, Junho Korea Atom Energy Res Inst Seoul South Korea Seoul Natl Univ Dept Civil & Environm Engn Seoul South Korea

Power supply network is one of the most critical infrastructure networks of urban communities, but prone to the risk of cascading failures. In efforts toward disaster risk reduction of urban communities, it is thus important to identify critical cascading failure scenarios of the power supply networks and prepare effective countermeasures based on the identified scenarios. While addressing these issues, previous research efforts focused on cascading failure scenarios induced by a single component although those induced by multiple components may occur under natural or man-made disaster events. A major challenge in this problem is high computational cost required for simulating cascading failure scenarios and solving large-size optimization problems. This paper first presents an effective method to identify multi-component failure combinations entailing critical cascading failures in the network by using the overload cascading model, the multi-group non-dominated sorting genetic algorithm (MG-NSGA), and the concept of critical zone. Based on the identified critical scenarios, cost-effective retrofit combinations against the risk of cascading failures are also identified using the proposed 'elite set updating' method. The proposed disaster risk reduction decision-making methods are demonstrated and tested by numerical examples of two complex power supply networks.

关键词： Power supply network Cascading failure scenarios Overload cascading failure multi-objective genetic algorithm Retrofit optimization

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Reduction of Insolvency Risk and Total Costs in Banking Sector using Partners Selection Approach with genetic algorithm and multilayer Perceptron Neural Network

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INTERNATIONAL JOURNAL OF ENGINEERING 2024年第8期37卷 1667-1690页

作者： Azarbad, M. Shojaie, A. A. Abdi, F. Ghezavati, V. R. Khalili-Damghani, K. Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran

Banking, a vital economic pillar worldwide, thrives with effective management, aiding economic growth. Mitigating risks and addressing cost control are key challenges. Prioritizing strategies to enhance performance in both risk management and cost efficiency is crucial for the banking sector's success and economic stability. One approach is to select partners in such a way that the risk of bank insolvency and total costs are reduced, and the capital adequacy of the bank is increased. So, in this work, we first created a mathematical model to achieve the above goals in the field of banking using the approach of selecting partners. In this model, three objective functions are considered for the optimal selection of partners, two of which aim to minimize risk and cost, and the last objective is to maximize capital adequacy. To solve this multi -objective model, we implemented an integrated intelligent system. A combination of a multiobjective genetic algorithm and a neural network was used in this system. A multilayer perceptron neural network is used to calculate the nondeterministic parameters based on the data from different periods. The proposed method was evaluated using a numerical example in MATLAB software. The obtained results and their comparison with one of the classic algorithms show the superiority and reliability of this intelligent system. Using this system, the optimal partners can be selected to achieve the set goals. The most important factors in the field of risk have been identified. Then, a meta -heuristic multiobjective algorithm (NSGA-II) along with an intelligent neural network system has been used to optimally select partners. According to this intelligent system, a suitable methodology is presented along with the optimization algorithm.

关键词： Banking Banks Insolvency Risk Selecting Partners multi-objective genetic algorithm multilayer Perceptron Neural Network

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A Study on Analysis of Design Variables in Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine

A Study on Analysis of Design Variables in Pareto Solutions ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Kudo, Fumiya Yoshikawa, Tomohiro Furuhashi, Takeshi Nagoya Univ Grad Sch Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781424478354

genetic algorithm (GA)[1] is one of the most effective methods in the application to optimization problems. Recently, multi-objective genetic algorithm (MOGA) is focused on in the engineering design field. In this field, the analysis of design variables in the acquired Pareto solutions, which gives the designers useful knowledge in the applied problem, is important as well as the acquisition of advanced solutions. This paper proposes a new visualization method using Isomap which visualizes the geometric distances of solutions in the design variable space considering their distances in the objective space. The proposed method enables a user to analyze the design variables of the acquired solutions considering their relationship in the objective space. This paper applies the proposed method to the conceptual design optimization problem of hybrid rocket engine and studies the effectiveness of the proposed method. It shows that the visualized result gives some knowledges on the features between design variables and fitness values in the acquired Pareto solutions.

关键词： Analysis of Design Variables Visualization Isomap multi-objective genetic algorithm Conceptual Design Optimization Problem of Hybrid Rocket Engine

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MOGA-UDTG: Automated Uniformly Distributed Testing Approach

MOGA-UDTG: Automated Uniformly Distributed Testing Approach

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Conference on Recent Advances in Engineering and Computational Sciences (RAECS)

作者： Choudhary, Kavita Purohit, G. N. ITM Univ Dept CSE & IT Gurgaon India Banasthali Vidyapeeth Dept AIM & ACT Banasthali India

ISBN: (纸本)9781479922918

Software Testing is the critical task for any software which helps in building trust and confidence in the product. Usually, the emphasis is on Manual Testing but to reduce cost there is a need of automation in software testing process. This paper proposes a method to generate automated uniformly distributed test cases based on the platform of multiobjective genetic algorithm (MOGA). The proposed method is analyzed on the domain of Pareto-Optimality, Dominance and multi-objective genetic algorithm.

关键词： multi-objective genetic algorithm MOGA UDTG Pareto Optimal Non-dominated Sorting Fitness function etc

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MOGA for multi-Level Fuzzy Data Mining

MOGA for Multi-Level Fuzzy Data Mining

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IEEE International Conference on Granular Computing (GrC)

作者： Chen, Chun-Hao Ho, Chi-Hsuan Hong, Tzung-Pei Lin, Wei-Tee Tamkang Univ Dept Comp Sci & Infonnat Engn Taipei Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781467323109;9781467323116

In this paper, we propose a multi-objective multi-Level genetic-Fuzzy Mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The two objective functions of each chromosome are then calculated. The first one is the summation of large 1-itemsets of each item in different concept levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated by these two objective functions. After the GA process terminates, various sets of membership functions could be used for deriving multiple-level fuzzy association rules according to decision maker. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

关键词： data mining multi-objective genetic algorithm multi-concept levels membership function fuzzy association rule

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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 (GMEPE/PAHCE)

作者： Ruano, M. G. Hajimani, E. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Coimbra CISUC P-3000 Coimbra Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9781509024865

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 Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions 4

Multi-Objective Sizing Optimization Method of Microgrid Cons...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Zhu, Xiang Peng, Chao Geng, Hua Tsinghua Univ Dept Automat Beijing Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (纸本)9781665489577

In this paper, we propose a multi-objective sizing optimization method to optimize the economy-environmental comprehensive benefits of the grid-connected microgrid. To be specific, we combine the comprehensive cost function and the pollutant emissions by CO2 (PEC) function of the grid-connected microgrid to construct the economy-environmental multi-objective optimization model. In order to effectively solve the multi-objective optimization model, a self-adaptive multi-objective genetic algorithm (SAMOGA) based on the pre-grouped roulette algorithm, adaptive crossover and mutation probabilities is proposed. Simulation experiments show the optimal results which are on the Pareto optimal front solved by SAMOGA under various circumstances. The optimal results reflect the restrictive relationship between comprehensive cost and carbon emissions, which means that the final sizing scheme of the microgrid can be determined with full consideration of the objectives.

关键词： microgrid multi-objective optimization carbon emissions multi-objective genetic algorithm

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Bottom-up Evolutionary Subspace Clustering

Bottom-up Evolutionary Subspace Clustering

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2010 IEEE World Congress on Computational Intelligence

作者： Vahdat, Ali Heywood, Malcolm Zincir-Heywood, Nur Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

ISBN: (纸本)9781424481262

The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective genetic algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.

关键词： attribute-wise lattice bottom-up evolutionary subspace clustering candidate subspace clusters combinatorial search genetic algorithms multi-objective genetic algorithm pattern clustering search problems

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