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Optimal positioning of wind turbines on Gokceada using multi-objective genetic algorithm

WIND ENERGY 2010年第4期13卷 297-306页

作者： Sisbot, Sedat Turgut, Oezgue Tunc, Murat Camdali, Uenal Abant Izzet Baysal Univ Fac Engn & Architecture Dept Mech Engn TR-14280 Bolu Turkey Yeditepe Univ Fac Engn Dept Syst Engn TR-34755 Istanbul Turkey

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokceada Island located at the north-east of Aegean Sea bearing full potential of wind energy generation. A multi-objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： wind farms multi-objective genetic algorithm optimum localization Gokceada Island

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Optimization of aluminium sheet hot stamping process using a multi-objective stochastic approach

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ENGINEERING OPTIMIZATION 2016年第12期48卷 2173-2189页

作者： Xiao, Wenchao Wang, Baoyu Zhou, Jing Ma, Wenyu Yang, Lei Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

关键词： aluminium hot stamping optimization of process parameters response surface methodology multi-objective genetic algorithm Monte Carlo simulation method

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Demand-oriented multi-objective planning method for electronic product technology development

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APPLIED MATHEMATICAL MODELLING 2016年第5-6期40卷 3620-3634页

作者： Chiang, Tzu-An Che, Z. H. Wang, Te-Wei Chen, Jian-Hong Natl Taipei Univ Business Dept Business Adm 321Sect 1Jinan Rd Taipei Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management 1Sect 3Zhongxiao E Rd Taipei 106 Taiwan Univ Illinois Dept Management Informat Syst One Univ Plaza Springfield IL 62703 USA Natl Taipei Univ Technol Dept Ind Engn & Management 1Sect 3Zhongxiao E Rd Taipei 106 Taiwan

In this study, a demand-oriented multi-objective planning method is proposed for electronic product technology development. The proposed method uses the analytical hierarchy process to analyze the relative importance of each customer demand. Subsequently, quality function deployment is used to analyze the relationships between customer demands and product technologies, as well as for evaluating the customer satisfaction level for each electronic product that is being implemented. By using a model to identify the life-cycle stage of the product technology, each electronic product technology is classified in order to determine how to obtain the technology. A multi-objective evaluation model of planning strategies is obtained for electronic product technology development to evaluate the performance of different planning strategies. A multi-objective genetic algorithm is utilized to help companies efficiently establish an optimal planning strategy for electronic product technology development with the maximum customer satisfaction level and minimum product development cost. Finally, a case study of a green smart phone development project is provided to demonstrate and verify the practical contributions of the proposed method. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Electronic product technology development Life-cycle stage of product technology multi-objective genetic algorithm New product development

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Thermodynamic investigation and multi-objective optimization for jet impingement cooling system with Al₂O₃/water nanofluid

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ENERGY CONVERSION AND MANAGEMENT 2016年 111卷 38-56页

作者： Lam, Prasanth Anand Kumar Prakash, K. Arul Indian Inst Technol Dept Appl Mech Fluid Mech Lab Madras 600036 Tamil Nadu India

In the present study, a multi-objective optimization procedure combining finite element modeling of impingement cooling system, Response Surface Approximation (RSA) of objective functions and optimization based on multi-objective genetic algorithm (MOGA), to achieve maximum heat transfer and minimum entropy generation is demonstrated. For the purpose, numerical simulations are performed for impingement cooling system with Al2O3/water nanofluid, to investigate the influence of Reynolds number (Re), non-dimensional channel height (H/L) and nanoparticle volume fraction (phi) on fluid flow, heat transfer and entropy generation. The simulated results illustrate that, a secondary recirculation bubble observed on upper surfaces of fourth (at Re >= 500) and fifth (for Re >= 800) heat sources, lead to an accumulation of heat. The magnitude of local Nusselt number (Nu) is found to be maximum along stagnation region whereas in the regions of secondary recirculation a minimum value is observed. Further, an increase in overall surface averaged Nusselt number ((Nu(ov)) over bar) and global Vital entropy generation (S-tot,S-Omega) is observed with increasing Re, phi and decreasing H/L. Subsequently, (Nu(ov)) over bar and S-tot,S-Omega are selected as objective functions and are modeled using RSA. Furthermore, MOGA has been implemented to obtain optimum configurations of impingement cooling system encapsulating in the functional space lying on the Pareto-optimal frontier where a trade-off between two performance parameters, (Nu(ov)) over bar Nuou and S-tot,S-Omega are obtained. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Impinging jet Al2O3/water nanofluid Entropy generation SUPG-finite element method Response Surface Approximation multi-objective genetic algorithm

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multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

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ENGINEERING OPTIMIZATION 2016年第7期48卷 1251-1274页

作者： Zhao An Lai Zhounian Wu Peng Cao Linlin Wu Dazhuan Zhejiang Univ Inst Proc Equipment Hangzhou 310003 Zhejiang Peoples R China

This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bezier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.

关键词： optimization low specific speed centrifugal pump multi-objective genetic algorithm neural network design of experiment

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The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

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ENERGY AND BUILDINGS 2016年 120卷 145-158页

作者： Ruano, Antonio E. Pesteh, Shabnam Silva, Sergio Duarte, Helder Mestre, Goncalo Ferreira, Pedro M. Khosravani, Hamid R. Horta, Ricardo Univ Algarve Fac Sci & Technol Campus Gambelas P-8005139 Faro Portugal Univ Lisbon Inst Super Tecn Ctr Intelligent Syst IDMEC P-1049001 Lisbon Portugal Univ Algarve Ctr Empresarial Gambelas EasySensing Intelligent Syst Campus GambelasPav A5 P-8005139 Faro Portugal Univ Lisbon Fac Ciencias LaSIGE P-1049001 Lisbon Portugal Rolear SA P-8001906 Faro Portugal

This paper introduces the Intelligent MBPC (IMBPC) HVAC system, a complete solution to enable Model Based Predictive Control (MBPC) of existing HVAC installations in a building. The IMPBC HVAC minimizes the economic cost needed to maintain controlled rooms in thermal comfort during the periods of occupation. The hardware and software components of the IMBPC system are described, with a focus on the MBPC algorithm employed. The installation of IMBPC HVAC solution in a University building is described, and the results obtained in terms of economical savings and thermal comfort obtained are compared with standard, temperature regulated control. (C) 2016 Elsevier B.V. All rights reserved.

关键词： HVAC Model-Based Predictive Control Predicted mean vote Neural networks multi-objective genetic algorithm Weather station Wireless sensor networks

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A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期51卷 757-767页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Shao, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.

关键词： Flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality Immune and entropy principle

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Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement

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ELECTRICAL ENGINEERING 2010年第6期92卷 227-237页

作者： Gitizadeh, M. Shiraz Univ Technol Sch Elect & Elect Engn Shiraz Iran

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission systems (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensator (TCSC) and static var compensator (SVC) are the utilized FACTS devices. Our objectives are: active power loss reduction, new introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, are considered in the optimum allocation. A multi-objective genetic algorithm (MOGA) is used to approach the Pareto-optimal front (non-dominated) solutions. In addition, the estimated annual load profile has been utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. IEEE 14-bus Network is selected to validate the performance and effectiveness of the proposed method.

关键词： FACTS devices allocation multi-objective optimization multi-objective genetic algorithm

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Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework

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NEUROCOMPUTING 2016年 205卷 53-63页

作者： Zhang, Chi Wei, Haikun Xie, Liping Shen, Yu Zhang, Kanjian Southeast Univ Sch Ahtomat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Jiangsu Peoples R China

Point predictions of wind speed can hardly be reliable and accurate when the uncertainty level increases in data. Prediction intervals (PIs) provide a solution to quantify the uncertainty associated with point predictions. In this paper, we adopt radial basis function (RBF) neural networks to perform interval forecasting of the future wind speed. A two-step method is proposed to determine the RBF connection weights in a multi-objective optimization framework. In the first step, the centers of the RBF are determined using the K-means clustering algorithm and the hidden-output weights of the RBF are pre-trained using the least squares algorithm. In the second step, the hidden-output weights are further adjusted by the non-dominated sorting genetic algorithm-II (NSGA-II), which aims at concurrently minimizing the width and maximizing the coverage probability of the constructed intervals. We test the performance of the proposed method on three real data sets, which are collected from different wind farms in China. The experimental results indicate that the proposed method can provide higher quality PIs than the conventional multi-layer perceptron (MLP) based methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Prediction interval Radial basis function neural network multi-layer perceptron multi-objective genetic algorithm Wind speed

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multi-objective optimization of collation delay and makespan in mail-order pharmacy automated distribution system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期83卷 475-488页

作者： Mei, Kevin Li, Debiao Yoon, Sang Won Ryu, Jong-hynn Fuzhou Univ Fuzhou 350002 Fujian Peoples R China SUNY Binghamton Binghamton NY USA Hongik Univ Sejong South Korea

In this research, the collation delay (CD) and makespan minimization problem in a mail-order pharmacy automated distribution (MOPAD) system are studied. The MOPAD systems, which are integrated with pharmaceutical auto-dispenser machines, auto-packer machines, and conveyor, are utilized to fulfill the increasing prescription demand in recent years. The motivation of this research is derived from the practical deadlock problem in a MOPAD system of the central fill pharmacies (CFP). Most of the customer orders consist of multiple medications, which need to be collated together before being packaged and shipped. The CD is defined as the fulfillment completion time difference between the first and last medications within the same order, which is a critical factor of the MOPAD systems throughput. When CD is minimized, the makespan often increases. Therefore, alternative scheduling solutions are often needed to balance the CD and makespan in the MOPAD system. This paper presents the trade-off solutions between minimizing CD and the makespan. Three multi-objective genetic algorithms with a three-tuples chromosome design, including Vector Evaluated genetic algorithm (VEGA), multi-objective genetic algorithm (MOGA), and non-dominated sorted genetic algorithm-II (NSGA-II), are implemented and compared under various system settings. Compared to the current implemented longest processing time (LPT) heuristic, three multi-objective genetic algorithms save the CD by 95.67 %, but only increase the makespan by 5.62 % on average. The results also show that the NSGA-II provided the best frontier.

关键词： Collation delay minimization Mail-order pharmacy automated distribution system multi-objective genetic algorithm Three-tuple chromosome

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