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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2020年第4期2卷 681-691页

作者： Abe, Takuto Fujiwara, Koichi Inoue, Takao Kubo, Takatomi Yamakawa, Toshitaka Nomura, Sadahiro Suzuki, Michiyasu Kano, Manabu Kyoto Univ Dept Syst Sci Kyoto 6068501 Japan Nagoya Univ Dept Mat Proc Engn Nagoya Aichi 4648601 Japan Yamaguchi Univ Dept Neurosurg Sch Med Yamaguchi 7538511 Japan Nara Inst Sci & Technol Dept Informat Sci Ikoma 6300192 Japan Kumamoto Univ Fac Adv Sci & Technol Kumamoto 8608555 Japan Yamaguchi Univ Epilepsy Ctr Yamaguchi 7550046 Japan

Focal brain cooling (FBC) is a treatment for refractory epilepsy to suppress epileptic discharges from an epileptic focus. Our wearable FBC system under development consists of a recirculating coolant apparatus and a battery located extracorporeally, and a cooling device made of titanium with water channels inside which is embedded in the skull. An optimal channel design of the cooling device is needed to cool the brain efficiently. Although clinical and animal studies are required to design the cooling device, the number of experiments should be reduced, and finite element (FE) simulation be utilized instead. However, trial-and-error simulations impose a heavy computational burden. One option to deal with this problem is to use surrogate modeling, which is a data-driven approach that mimics the behavior of simulation models with a low computational burden. We optimized the channel structure by combining the surrogate model approach and non-dominated sorting genetic algorithm ii (NSGA-ii), which is a multi-objective optimization algorithm. The objective was to design a cooling device that simultaneously achieves high cooling performance and low pressure drop since low pressure loss contributes to miniaturization and energy saving of the recirculating apparatus. The optimization result showed that pressure loss was reduced by 28.7% in comparison with the design variables determined through experiments without deteriorating the cooling performance.

关键词： Finite element simulation focal brain cooling surrogate model non-dominated sorting genetic algorithm ii optimization

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Design and Optimization for a Novel 3D Flux Micro-machine used in Trajectory Correction Fuze

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ENGINEERING LETTERS 2019年第3期27卷 516-526页

作者： Hui, Jianghai Gao, Min Li, Chaowang Ke, Zhifei Army Engn Univ Shijiazhuang Campus97 Heping West Rd Shijiazhuang 050003 Hebei Peoples R China

This paper proposed a novel permanent magnet machine with 3D flux path for the trajectory correction fuze, which is used to correct the projectile's trajectory during flight. First, the topology structure and equivalent magnetic circuit model were described, and the 3D flux path was portrayed. To conduct the torque and generated power optimization, the geometric parameters which influence the performance were found out by the relationship between the geometric parameters and performance. Second, the machine's flux-concentrating effect and magnetic distribution were achieved. The breaking torque, load voltage and stator current were also acquired by preliminary simulation with finite element model. Based on the sample points produced by orthogonal design and the range analysis results, the three approximation models were created, from which the Kriging model was selected to represent the simulation. Third, non-dominated sorting genetic algorithm ii and multi-objective particle swam optimization were adopted to optimize the breaking torque and generated power. The optimal performance and the corresponding geometric parameters were acquired after optimization by the algorithms. In addition to this, to compare the optimization results from the two optimization algorithm and verify their correctness, the breaking torque and generated power under five different speed discrepancy with respect to the projectile's flight velocity were obtained by simulation based on the optimized machine's model. Finally, the prototype experiment was conducted to verify the optimization method and simulation. The results indicated that the proposed machine which had been optimized can meet the performance acquirement of trajectory correction.

关键词： machine breaking torque generated power design of experiment approximation model non-dominated sorting genetic algorithm ii multi-objective particle swam optimization

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Multi-objective Optimization Approach for Allocation and Sizing of Distributed Energy Resources Preserving the Protection Scheme in Distribution Networks

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2023年第5期34卷 1080-1092页

作者： Ferraz, Renato S. F. Ferraz, Rafael S. F. Rueda-Medina, Augusto C. C. Univ Fed Espirito Santo Postgrad Program Elect Engn Av Fernando Ferrari BR-29075910 Vitoria ES Brazil

In this paper, a multi-objective optimization approach to solve the problem of optimal allocation and sizing of inverter-based distributed energy resources (DERs) in distribution systems is presented. The objectives of this allocation and sizing problem consist of the minimization of the investment and operation costs, the voltage deviation and the short-circuit currents. The recloser-fuse coordination constraints were included in the mathematical formulation of the problem, in order to preserve the original network protection scheme. It is important to mention that this protection scheme was also carried out in this paper, using the multi-objective approach, to minimize the operating time of the reclosers and fuses. The distribution system considered to evaluate the proposed methodology was the IEEE 34-Node Test Feeder, and the non-dominated sorting genetic algorithm ii was used to solve the proposed multi-objective problems. Furthermore, a time-series-based probabilistic approach, through the Monte Carlo simulation, was adopted to deal with the uncertainties of the load and power generated by each DER. Finally, from the results, it was possible to reduce the investment and operation costs by 15.61% when compared to the system without DERs, improve the voltage profile and preserve the original protection scheme present in the network.

关键词： Distributed energy resource non-dominated sorting genetic algorithm ii Protection system Short-circuit currents

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Evolutionary Multiobjective Optimization for Portfolios in Emerging Markets: Contrasting Higher Moments and Median Models 19th

Evolutionary Multiobjective Optimization for Portfolios in E...

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19th European Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： Ibrahim, Mai A. El-Beltagy, Mohammed Khorshid, Motaz Cairo Univ Fac Comp & Informat Operat Res & Decis Support Dept Giza Egypt

ISBN: (纸本)9783319312040

Multi-objective Evolutionary algorithms are well suited to Portfolio Optimization and hence have been applied in complex situations were traditional mathematical programming falls short. Often they were used in portfolios scenario of classical Mean-Variance which are not applicable to the Emerging Markets. Emerging Markets are characterized by return distributions that have shown to exhibit significance departure from normality and are characterized by skewness and fat tails. Therefore higher moments models and median models have been suggested in the literature for asset allocation in this case. Three higher moment models namely the Mean-Variance-Skewness, Mean-Variance-Skewness-Kurtosis, Mean-Variance-Skewness-Kurtosis for return and liquidity and three median models namely the Median-Value at Risk, Median-Conditional Value at Risk and Median-Mean Absolute Deviation are formulated as a multi-objective problem and solved using a multi-objective evolutionary algorithm namely the non-dominated sorting genetic algorithm ii. The six models are compared and tested on real financial data of the Egyptian Index EGX. The median models were found in general to outperform the higher moments models. The performance of the median models was found to be better as the out-sample time increases.

关键词： Median models Higher moment models Multi-objective evolutionary optimization non-dominated sorting genetic algorithm ii Egyptian stock exchange

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Feature Extraction of PV Output for Optimal Design of a PV-ES Combined System

Feature Extraction of PV Output for Optimal Design of a PV-E...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Wang, Xiaochen Shi, Zhaohui Tang, Liang Shi, Jie Liu, Lian Guo, Kangzhuang Shandong Elect Power Engn Consulting Inst Corp Lt Jinan Peoples R China Univ Jinan Sch Phys & Technol Jinan Peoples R China Shandong Luruan Tech Corp Ltd Jinan Peoples R China

ISBN: (纸本)9798350339345

Photovoltaic power output has apparent periodicity according to the solar power by nature, and this feature always shows different fluctuation features depending on various weather conditions, which bring much challenge when it is integrated to grid. Combine Energy Storage System (ESS) to power station so as to establish a PV-ES Combined System is one of the appropriate solutions. As ESS is costly according to current technology, the capacity and power flow should be sized considering the economic and efficient issues. In this paper, after weather type clustering, the fluctuation features of real-time solar power output data are studied according to Combined Empirical Mode Decomposition (CEMD) to optimized design a Hybrid Energy Storage System (HESS). In addition, non-dominated sorting genetic algorithm ii (NSGA ii) is applied to optimize the PV-ESS generation scheduling process by means of minimizing deviation between generation plan and real-time integrated power output. Case studies show that the proposed HESS system along with the optimization performs better using RBFNN than the one using BPNN power forecasting models. Besides, the case also illustrates the advantage of the proposed method keeping energy storage units' health and safety well.

关键词： Hybrid Energy Storage System (HESS) Generation Scheduling PV-ES Combined Generation System non-dominated sorting genetic algorithm ii

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Optimizing Resource Allocation to Edge Devices in Edge Computing: Exploring Multi-objectives Optimization Techniques 9th

Optimizing Resource Allocation to Edge Devices in Edge Compu...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Hoa Doan Nguyen Thanh Phu Nguyen Ngoc Thien Bang Bui Cong VSB Tech Univ Ostrava Dept Comp Sci 17 Listopadu 15 Ostrava Czech Republic FPT Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9789819732982;9789819732999

In the burgeoning domain of edge computing, efficient resource allocation to edge devices plays a pivotal role in enhancing system performance and user experience. This paper delves into the intricacies of optimizing resource allocation for edge devices by exploring multi-objective optimization techniques. We specifically focus on three crucial goals: minimizing distance, maximizing processing capacity, and maximizing the probability of success for each edge device. Through extensive experimentation and analysis, we demonstrate the effectiveness of employing the non-dominated sorting genetic algorithm ii(NSGA-ii) in achieving optimal solutions for these objectives. Our findings showcase that the NSGA-ii algorithm outperforms other methods, providing a compelling demonstration of its efficacy in addressing the challenges associated with multi-objective resource allocation in edge computing environments. This paper not only contributes valuable insights into the realm of edge computing but also serves as a practical guide for researchers and practitioners seeking to optimize resource allocation in similar contexts.

关键词： Edge computing Resource allocation Multi-objective optimization non-dominated sorting genetic algorithm ii

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Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

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China Welding 2023年第3期32卷 28-35页

作者：马生明张爱华顾建军漆宇晟马晶王平 College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Department of Electrical and Computer Engineering Dalhousie UniversityHalifaxNSB3J 2X4 Canada

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base *** welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.

关键词： ultra-narrow gap optimization of process parameters non-dominated sorting genetic algorithm ii the sidewall fusion depth

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A Two-Layer Model for Vehicle Routing Problem Based on genetic algorithm

A Two-Layer Model for Vehicle Routing Problem Based on Genet...

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IEEE International Conference on Service Operations and Logistics and Informatics

作者： Xu, Xin Fan, Wenhui Wang, Wei Xu, Huayu Tsinghua Univ Natl CIMS Engn Res Ctr Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781424420124

The Vehicle Routing Problem's focal point is no longer a single objective to get a shortest routing or satisfy customer's time demand, but a multiple objective which include nearly every aspect in the distribution network such as the cost of delivery, shipping time, and distance of transportation. Most of these aspects are very significant in the delivery network. Classic VRP models cannot take real-time reaction to the traffic jam. In this paper, a two-layer model including multiple objective and real-time road information is established. The genetic algorithm, NSGA ii and ESGA, is used to optimize this model. This optimization algorithm could find the optimal result very soon.

关键词： VRP non-dominated sorting genetic algorithm ii elite selected genetic algorithm

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An Improved NSGA-ii for Solving Reentrant Flexible Assembly Job Shop Scheduling Problem 14th

An Improved NSGA-II for Solving Reentrant Flexible Assembly ...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Wu, Xiuli Zhang, Yaqi Zhao, Kunhai Univ Sci & Technol Beijing Coll Mech Engn 30 Xueyuan Rd Beijing Peoples R China

ISBN: (纸本)9783031366215;9783031366222

In the wafer manufacturing process of micro electro mechanical systems (MEMS), there are reentrant flow, parallel machines, and assembly operation. Therefore, this study models its scheduling problem as a reentrant flexible assembly job shop scheduling problem. First, a mathematical model is formulated to minimize the total tardiness and the total energy consumption. Second, an improved non-dominated sorting genetic algorithm ii (INSGA-ii) is proposed to solve this NP-hard problem. An encoding and decoding method are designed according to the problem characteristics. A rule-based initialization strategy is developed to improve the quality of the initialized population. Specific crossover, mutation and selection operators are designed. Finally, numerical experiments are carried out, and the result shows that the proposed algorithm can effectively solve the problem.

关键词： MEMS Wafer Manufacturing Reentrant Flexible Assembly Job Shop Scheduling non-dominated sorting genetic algorithm ii

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non-dominated sorting genetic Filter A Multi-objective Evolutionary Particle Filter

Non-dominated Sorting Genetic Filter A Multi-objective Evolu...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Heris, S. Mostapha Kalami Khaloozadeh, Hamid KN Toosi Univ Technol Fac Elect & Comp Engn Dept Control Engn Tehran Iran

ISBN: (纸本)9781479933501

In this paper, the problem of nonlinear state estimation converted to a multi-objective optimization problem, and based on non-dominated genetic algorithm ii (NSGA-ii) and Particle Filter (PF), a multi-objective evolutionary particle filter, namely non-dominated genetic Filter (NSGF) is proposed. Search and optimization abilities of NSGA-ii are incorporated into standard particle filtering framework to improve the estimation performance. Classic filtering approaches define a single criterion to evaluate an estimated state vector, however in this paper, two criteria are defined to evaluate and rate estimated state vectors. Conversion of the state estimation problem into a multi-objective optimization problem, improves diversity of promising solutions, and finally improves the estimation performance. Simulation results are given for an example and NSGF is compared to other types of particle filters. Efficiency and applicability of NSGF is confirmed according to the obtained results.

关键词： Particle Filter Multiobjective Optimization non-dominated sorting genetic algorithm ii Evolutionary Filtering nonlinear Filtering State Estimation

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