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30th International Power System Conference (PSC)

作者： Elhaminia, Pedram Moradnouri, Ahmad Vakilian, Mehdi Sharif Univ Technol Ctr Excellence Power Syst Management & Control Elect Engn Dept Tehran Iran

ISBN: (纸本)9781509027057

A wind turbine transformer (WTT) is designed using a 3D wound core while the transformer's total owning cost (TOC) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. multi-objective genetic algorithm is utilized to derive Pareto optimal solutions. The effects of inrush current improvement on other operating and design parameters of the transformer such as: losses, dimensions, and weights are investigated. An approach is presented to select one design from optimal Pareto solutions based on relative improvement of the inrush current performance. Finally, this multi-objective optimum wind turbine transformer design is compared with an optimum transformer design obtained when just TOC is the objective function.

关键词： wind turbine transformer (WTT) 3D wound core total owning cost (TOC) inrush current multi-objective genetic algorithm Pareto optimal solutions

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Improving a neural networks based HVAC predictive control approach 9

Improving a neural networks based HVAC predictive control ap...

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IEEE 9th International Symposium on Intelligent Signal Processing (WISP)

作者： Ruano, A. E. Silva, S. Pesteh, S. Ferreira, P. M. Duarte, H. Mestre, G. Khosravani, H. Horta, R. Univ Algarve Faro Portugal EasySensing Ltd Coimbra Portugal IST IDMEC Ctr Intelligent Syst Lisbon Portugal Rolear SA Faro Portugal Univ Lisbon Fac Sci LaSIGE P-1699 Lisbon Portugal

ISBN: (纸本)9781479972524

This paper improves an existing Model Based Predictive Control Approach (MBPC), applied for Heating Ventilation and Air Conditioning (HVAC) control in buildings. The existing approach uses the Predictive Mean Vote (PMV) to assess thermal comfort. It has been found that PMV estimation and forecasts deteriorate when the room is occupied. In order to solve this problem, we propose to incorporate measurements of activity inside the room in the predictive models of the inside air temperature. Another improvement to the existing approach is to use an economic cost function, reflecting the money needed for the HVAC control, instead of a cost function related with the consumption of energy.

关键词： HVAC Predictive Control Predicted Mean Vote Neural Networks multi-objective genetic algorithm Thermal Comfort Room Occupancy Economic Cost Function

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A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach

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Journal of Mathematical Modelling and algorithms in Operations Research 2015年第3期14卷 287-312页

作者： Khalili-Damghani, Kaveh Abtahi, Amir-Reza Ghasemi, Amir Department of Industrial Engineering South-Tehran Branch Islamic Azad University Tehran Iran Department of Knowledge Engineering and Decision Sciences Faculty of Management Economics University of Economic Sciences Tehran Iran

Supply, manufacture, and distribution of perishable products are challenging jobs in supply chains. Location of warehouses and routing of vehicles are essential issues to distribute perishable products properly. In this paper, a new bi-objective mixed integer mathematical programming is proposed to reduce the total cost of the supply chain and to balance the workload of distribution centers while the due dates of delivery of perishable product are met, concurrently. The considered properties and constraints of proposed model made it well-posed to illustrate the real life situation. As the proposed model is NP-Hard, an evolutionary algorithm called Non-Dominated Sorting genetic algorithm-II (NSGA-II) is customized to solve the problem. The structure of chromosome and genetic operators are customized for the problem. The performance of proposed NSGA-II and an efficient exact multi-objective method, called ε-constraint, is compared using accuracy and diversity metrics on several benchmark instances. © 2015, Springer Science+Business Media Dordrecht.

关键词： Bi-objective optimization Distribution of perishable products Location-routing problem Mixed-integer programming multi-objective genetic algorithm ε-constraint Method

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Real-time online Compensation the Nonlinearities and Uncertainties in Missile Servo Control System 27

Real-time online Compensation the Nonlinearities and Uncerta...

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27th Chinese Control and Decision Conference (CCDC)

作者： Feng, Zhang Kun Wei, Rao Da Bo, Zhu Jian Hua, Duan Li Luoyang Optoelect Technol Dev Ctr Luoyang 471009 Peoples R China

ISBN: (纸本)9781479970162

To restrain the influences of the nonlinearities and uncertainties in missile servo control systems, the active disturbance rejection control (ADRC) technique is introduced to real-time online estimate and compensate these factors. As to the problem of the difficulties in tuning ADRC parameters, an improved multi-objective genetic algorithm is adopted to solve the complete set of Pareto solutions for the control parameters, then a multi-criteria decision making algorithm based on utility function is chosen to determine the fittest solution. Then a test platform using TMS320C28346 floating-point DSP as control core is built to verify the research results. The experimental results show that the optimal ADRC parameter is appropriate for the servo control system. The system tracks the input commands smoothly and reliably, and has good robustness and adaptability to nonlinearities and parameters drifts. Also the strategy for tuning the ADRC parameters is practical and feasible.

关键词： Nonlinearity and Uncertainty Active Disturbance Rejection Control multi-objective genetic algorithm

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multi-item Supplier Selection Model with Fuzzy Risk Analysis Studied by Possibility and Necessity Constraints

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FUZZY INFORMATION AND ENGINEERING 2015年第4期7卷 451-474页

作者： Patra, Kartik Mondal, Shyamal Kumar Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 WB India

Three different supplier selection models have been developed in crisp and fuzzy environments. Here two objective functions have been considered, profit and risk. In this paper, profit has been maximized and risk has been minimized with some constraints. Each supplier has an limited capacity. The purchasing cost of each item from different supplier as well as associative risk is known. The total space and budget of a retailer are constant. In Model I, all the parameters are considered as crisp. In Model II, the demand has been considered as fuzzy. In Model III, the risk values and demand have been considered as fuzzy. To defuzzyfy the fuzzy constraints, necessity and possibility have been introduced. To defuzzyfy the fuzzy objective, two different methods, credibility measure and alpha-cut method have been introduced. All the models have been illustrated numerically using multi-objective genetic algorithm (MOGA). Also a sensitivity analysis has been done taking different sets of risk values and a comparison result has been shown for credibility measure and alpha-cut-method-for Model-III.

关键词： Supplier selection Necessity Possibility Risk multi-objective genetic algorithm

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Evolutionary feature selection for emotion recognition in multilingual speech analysis

Evolutionary feature selection for emotion recognition in mu...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Brester, Christina Semenkin, Eugene Kovalev, Igor Zelenkov, Pavel Sidorov, Maxim Siberian State Aerosp Univ Inst Comp Sci & Telecommun Krasnoyarsk Russia Univ Ulm Inst Commun Engn Ulm Germany

ISBN: (纸本)9781479974924

In the case when conventional feature selection methods do not demonstrate sufficient performance, alternative algorithmic schemes might be applied. In this paper we propose an evolutionary feature selection technique based on the two-criteria optimization model. To diminish the drawbacks of genetic algorithms, which are used as optimizers, we design a parallel multi-criteria heuristic procedure based on an island model. The effectiveness of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the crucial aspects in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were engaged in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 11.15% relative improvement compared with the best F-score value on the full set of attributes).

关键词： feature selection multi-objective genetic algorithm island model emotion recognition

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Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1756-1766页

作者： Jia, Jie Chen, Jian Chang, Guiran Tan, Zhenhua Northeastern Univ Coll Informat Sci & Engn 402 Sch Informat Sci & Engn Shenyang 110004 Liaoning Prov Peoples R China Northeastern Univ Ctr Comp Shenyang 110004 Peoples R China Northeastern Univ Sch Software Shenyang 110004 Peoples R China

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Coverage control Energy multi-objective genetic algorithm Wireless sensor network

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Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

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ENVIRONMENTAL MODELLING & SOFTWARE 2009年第4期24卷 530-541页

作者： Behzadian, Kourosh Kapelan, Zoran Savic, Dragan Ardeshir, Abdollah Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Amir Kabir Univ Technol Environm Res Ctr Tehran Iran Amir Kabir Univ Technol Dept Civil & Environm Engn Tehran Iran

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Water distribution system Calibration Uncertainty multi-objective genetic algorithm Adaptive neural networks

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Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2009年第1-2期499卷 325-328页

作者： Liu Wei Yang Yuying Xing Zhongwen Zhao Lihong Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

An automated optimization method by the integration of the response-surface method and an optimization algorithm is presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, a multi-objective genetic algorithm is applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. The response-surface model is employed as a fast analysis tool to surrogate the time-consuming finite-element procedure in the iterations of the multi-objective genetic algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is more efficient than the traditional manual finite-element procedure and the 'trial and error' approach for springback controlling. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Springback Sheet metal Response-surface method multi-objective genetic algorithm

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Thermo-economic triple-objective optimization of a solar chimney power plant using genetic algorithms

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ENERGY 2014年 70卷 204-211页

作者： Gholamalizadeh, Ehsan Kim, Man-Hoe Kyungpook Natl Univ Sch Mech Engn Taegu South Korea

A triple-objective design method was developed for a solar chimney power plant system that simultaneously optimizes the expenditure, total efficiency, and power output. A multi-objective genetic algorithm was used to obtain the best combination of geometric parameters of the power plant. The following design parameters were selected: collector radius, chimney height, and chimney diameter. Two different solar chimney power plant configurations were considered: the Kerman pilot power plant and Manzanares prototype power plant. A set of possible optimal solutions (Pareto optimal set) was obtained. Based on the optimal solutions, the best configuration for each power plant was selected. The performance and expenditure of the optimal solutions and the built power plants were compared. The results showed that the increment of the power output was higher than the increment of the expenditure in the optimal configuration. A parametric study was conducted to evaluate the effects of changing design parameters on different objective functions. This paper provides a very useful design and optimization methodology for solar chimney power plant systems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar chimney power plant Renewable energy Thermo-economic analysis Optimization multi-objective genetic algorithm

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