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Development of an effective data-driven model for hourly typhoon rainfall forecasting

JOURNAL OF HYDROLOGY 2013年 495卷 52-63页

作者： Lin, Gwo-Fong Jhong, Bing-Chen Chang, Chia-Chuan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

In this paper, we proposed a new typhoon rainfall forecasting model to improve hourly typhoon rainfall forecasting. The proposed model integrates multi-objective genetic algorithm with support vector machines. In addition to the rainfall data, the meteorological parameters are also considered. For each lead time forecasting, the proposed model can subjectively determine the optimal combination of input variables including rainfall and meteorological parameters. For 1- to 6-h ahead forecasts, an application to high- and low-altitude metrological stations has shown that the proposed model yields the best performance as compared to other models. It is found that meteorological parameters are useful. However, the use of the optimal combination of input variables determined by the proposed model yields more accurate forecasts than the use of all input variables. The proposed model can significantly improve hourly typhoon rainfall forecasting, especially for the long lead time forecasting. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Typhoon rainfall forecasting Support vector machine multi-objective genetic algorithm Meteorological parameters

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The optimization of the key components of the spinning machine conveyor system based on ISIGHT

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2nd International Conference on Textile Engineering and Materials (ICTEM2012)

作者： Lin, Bin Zhang, Xu Liu, Xiaogang Tianjin Univ State Educ Minist Key Lab Adv Ceram & Machining T Tianjin 300072 Peoples R China

ISBN: (纸本)9783037855751

A transfer system in roving-spinning unit that establishes a connection between spindles of roving frame and spinning frame achieves the manipulations automatically in spinning workshop. We study that ISIGHT drive ANSYS to conduct integrated optimization of the key conveyor system components in this paper, the ANSYS-ISIGHT integration is successful. Before the optimal design, using the Latin method to design experiments. The experimental design can show the degree that the state variables and the target variables are affected by each input factor. The factor that has little impact on the results can be removed, thus the efficiency of the optimization can be improved. After the experimental design, using the multi-objective neural network to optimize the beam. Giving the beam safety conditions, including intensity, toughness, modal, stability. We optimize the structure base on security conditions, the volume of the beam in roving and yarn reservoir area has been largely decreased. Achieving the objectives of the lightweight design.

关键词： ISIGHT-ANSYS integration Latin method Optimization iterations multi-objective genetic algorithm

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Surrogate-Based multi-objective Optimization and Data Mining of Vortex Generators on a Transonic Infinite-Wing

Surrogate-Based Multi-Objective Optimization and Data Mining...

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IEEE Congress on Evolutionary Computation

作者： Namura, Nobuo Obayashi, Shigeru Jeong, Shinkyu Tohoku Univ Inst Fluid Sci Sendai Miyagi 9808577 Japan Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9781479904549;9781479904532

multi-objective optimization and data mining of vortex generators (VGs) on a transonic infinite-wing was performed using computational fluid dynamics (CFD), surrogate models, and a multi-objective genetic algorithm (MOGA). VGs arrangements were defined by five design variables: height, length, incidence angle, spacing, and chord location. The objective functions which should be maximized were three: lift-drag ratio at low angle of attack, lift coefficient at high angle of attack, and chordwise separation location at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the response surface methodology with Kriging model and the modified version of it was employed because CFD analysis of the wing with VG requires a large computational time. Two types of data mining method: analysis of variance (ANOVA) and self-organizing map (SOM), were applied to the result of the optimization. It was revealed by ANOVA that the ratio of spacing to height and the incidence angle had significant influences to maximizing each objective function. By using SOM, VG designs were split into four types which have different aerodynamic characteristics respectively. The appropriate values of parameters were identified by SOM.

关键词： multi-objective genetic algorithm Kriging model radial basis function networks self-organizing map analysis of variance computational fluid dynamics

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Improving an Evolutionary multi-objective algorithm for the Biclustering of Gene Expression Data

Improving an Evolutionary Multi-objective Algorithm for the ...

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IEEE Congress on Evolutionary Computation

作者： Brizuela, Carlos A. Luna-Taylor, Jorge E. Martinez-Perez, Israel Guillen, Hugo A. Rodriguez, David O. Beltran-Verdugo, Armando CICESE Dept Comp Sci Ensenada Baja California Mexico ITLP Dept Syst & Computat La Paz Mexico

ISBN: (纸本)9781479904549;9781479904532

The development of new technologies for the design of DNA microarrays has boosted the generation of large volumes of biological data, which requires the development of efficient computational methods for their analysis and annotation. Among these methods, biclusters generation algorithms attempt to identify coherent associations of genes and experimental conditions. In this paper, we introduce an improved version of a multi-objective genetic algorithm to find large biclusters that are, at the same time, highly homogeneous. The proposed improvement uses a group based representation for the genes-conditions associations rather than long binary strings. To assess the proposal performance the algorithm is applied to generate biclusters for two real gene expression data: Saccharomyces Cerevisiae with 2884 genes and 17 conditions, and the human B cells Lymphoma with 4026 genes and 96 conditions. The results of computational experiments show that the proposed approach outperforms current state-of-the-art algorithms on these data sets.

关键词： biclustering gene expression multi-objective genetic algorithm group based representation microarray DNA

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Portfolio management using computational intelligence approaches: Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary algorithms

Portfolio management using computational intelligence approa...

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作者： Prisadarng SKOLPADUNGKET University of Bradford

学位级别：博士

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of multi-objective genetic algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated genetic algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

关键词： Portfolio optimisation Realistic constraints multi-objective genetic algorithm estimation error model risk Fuzzy model selection Strength Pareto Evolutionary algorithm 2

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"Load Forecasting on the User‐side by means of Computational Intelligence algorithms"

"Load Forecasting on the User‐side by means of Computationa...

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作者： Juan Jose Cardenas Araujo Universitat Politecnica de Catalunya

学位级别：博士

Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs

关键词： Energy efficiency Load profile Load forecasting User side Energy management system Intelligent energy management system Load modeling and forecasting system Adaptability Autonomy Computational intelligence ANFIS ANN genetic algorithm multi-objective genetic algorithm Hilbert-Huang transform Hilbert-spectrum analysis

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Antenna Optimization in Long-Term Evolution Networks

Antenna Optimization in Long-Term Evolution Networks

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作者： Deng, Qichen KTH Royal Institute of Technology

学位级别：master

The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization algorithm, Random Search algorithm, Modied Steepest Descent algorithm and multi-objective genetic algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report

关键词： multi-objective Optimization Non Dominated (Pareto Optimal) Solutions Pareto Front Cost Function Stepwise Minimization algorithm Random Search algorithm Modied Steepest Descent algorithm multi-objective genetic algorithm Graphical User Interface (GUI). Natural Sciences Mathematics Naturvetenskap Matematik Physics Chemistry Mathematics fysik/kemi/matematik Master of Science - Mathematics Teknologie masterexamen - Matematik Mathematics Matematik

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multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using genetic algorithm

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TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2012年第4期55卷 229-236页

作者： Kitagawa, Yosuke Kitagawa, Koki Nakamiya, Masaki Kanazaki, Masahiro Shimada, Toru Tokyo Metropolitan Univ Dept Aeronaut & Astronaut Hino Tokyo Japan Japan Aerosp Explorat Agcy ISAS JAXA Sagamihara Kanagawa Japan

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

关键词： Hybrid Rocket multidisciplinary Design Optimization multi-objective genetic algorithm

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multi-objective genetic algorithms, NSGA-II and SPEA2, for Document Clustering

Multi-Objective Genetic Algorithms, NSGA-II and SPEA2, for D...

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International Conference on Advanced Software Engineering and Its Applications (ASEA 2011)

作者： Lee, Jung Song Choi, Lim Cheon Park, Soon Cheol Jeonbuk Natl Univ Div Elect & Informat Engn Jeonju Jeonbuk South Korea

ISBN: (纸本)9783642272066

This paper proposes the multi-objective genetic algorithm (MOGA) for document clustering. The studied, hierarchical agglomerative algorithms. k-means algorithm and general genetic algorithm (GA) are more progressing in document clustering. However, in hierarchical agglomerative algorithms, efficiency is a problem (0(n(2)logn)), k-means algorithm depends on too much the initial centroids, and general GA can converge to the local optimal value when defining an objective function which is not suitable. In this paper, two of MOGA's algorithms, NSGA-II and SPEA2 are applied to document clustering in order to complete these disadvantages. We compare to NSGA-II. SPEA2 and the existing clustering algorithms (k-means. general GA). Our experimental results show the average values of NSGA-II and SPEA2 are about 28% higher the clustering performance than the k-means algorithm and about 17% higher the clustering performance than the general GA.

关键词： Document Clustering genetic algorithm multi-objective genetic algorithm NSGA-II SPEA2

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A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

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ENGINEERING OPTIMIZATION 2012年第3期44卷 305-325页

作者： Velasco, N. Dejax, P. Gueret, C. Prins, C. Univ Los Andes Dept Ingn Ind Bogota Colombia Ecole Mines Nantes UMR 6597 IRCCyN F-44307 Nantes 3 France Univ Technol Troyes FRE 2848 ICD F-10010 Troyes France

Some companies must transport their personnel within facilities. This is especially the case for oil companies that use helicopters to transport engineers, technicians and assistant personnel from platform to platform. This operation has the potential to become expensive if the transportation routes are not correctly planned and provide a bad quality of service. Here this issue is modelled as a pick-up and delivery problem where a set of transportation requests should be scheduled in routes, minimizing the total transportation cost while the most urgent requests are satisfied by priority. To solve the problem, a method based on a Non-dominated Sorting genetic algorithm (NSGA-II) is proposed. This algorithm is tested on both randomly generated and real instances provided by a petroleum company. The results show that the proposed algorithm improves the best-known solutions.

关键词： routing pick-up and delivery personnel transportation non-dominated sorting algorithm multi-objective genetic algorithm

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