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Spare optimization Using Genetic algorithms

Spare Optimization Using Genetic Algorithms

引用

international workshop on Intelligent Systems and applications, ISA

作者： Xiaoli Zou Dept. of Civil Eng. Wuhan Polytech. Univ. Wuhan China

Spare optimization models for systems with components connected in series and parallel are presented. The component availability at any time is obtained through Poisson process theory and the availability of the series-parallel system is written by system reliability analysis method. The objective of the optimization problem is to maximize the availability of the system satisfying the constraint on cost. Genetic algorithms are used to find the optimal amount of spares for each component. Finally, two illustrative examples are given.

关键词： Genetic algorithms Availability Dynamic programming Constraint optimization Aerospace industry Space technology Linear programming Civil engineering Reliability Time series analysis

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New Feature Splitting Criteria for Co-training Using Genetic Algorithm optimization

New Feature Splitting Criteria for Co-training Using Genetic...

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9th international workshop on Multiple Classifier Systems

作者： Salaheldin, Ahmed El Gayar, Neamat Nile Univ Ctr Informat Sci Giza Egypt

ISBN: (纸本)9783642121265

Often in real world applications only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. Co-training is a popular semi-supervised learning technique that uses a small set of labeled data and enough unlabeled data to create more accurate classification models. A key feature for successful co-training is to split the features among more than one view. In this paper we propose new splitting criteria based on the confidence of the views, the diversity of the views, and compare them to random and natural splits. We also examine a previously proposed artificial split that maximizes the independence between the views, and propose a mixed criterion for splitting features based on both the confidence and the independence of the views. Genetic algorithms are used to choose the splits which optimize the independence of the views given the class, the confidence of the views in their predictions, and the diversity of the views. We demonstrate that our proposed splitting criteria improve the performance of co-training.

关键词： Genetic algorithms

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Optimal Design of Structures with Discrete Variables Based on Improved Genetic algorithms

Optimal Design of Structures with Discrete Variables Based o...

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international workshop on Intelligent Systems and applications, ISA

作者： Guofu Sun Yanhui Ge Sch. of Bus. Adm. Shandong Univ. of Finance Jinan China Sch. of Civil Engneering Shandong Jiaotong Univ. Jinan China

In order to overcome premature phenomenon of simple genetic algorithms and inability to optimize algorithms with complex constraints, an improved genetic algorithms based on some improved methods is presented in this paper and is applied in optimization design of frame structure by adopting adaptive crossover rate and mutation rate, adjusting population size, fitness and penalty function and elitist strategy in the search of GA. The experimental results indicate that the improved genetic algorithm has good performance on the global convergence and that the proposed method can be applied to optimal design of structures with discrete variables.

关键词： Algorithm design and analysis Genetic algorithms Constraint optimization Steel Design optimization Genetic mutations Stress Convergence Biological cells Sun

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An Attribute Reduction Algorithm of the Information System Based on DNA-Sticker Model

An Attribute Reduction Algorithm of the Information System B...

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international workshop on Intelligent Systems and applications, ISA

作者： Shu-Xin Tian Xiao-Ping Wu Hong-Xia Wang Electronic Engineering College Naval University of Engineering Wuhan China

Aiming at the attribute reduction problems of the information system, a new algorithm based on DNA-sticker model is proposed in this paper. Firstly, the dependency matrix of the information system is established, which could convert the problem of attribute reduction to set cover problem. Then, the all true solution can be achieved by using DNA-sticker model. Finally, through the example the conclusion is drawn that this method is feasible, simple, and effective.

关键词： Information systems Educational institutions Set theory Ant colony optimization Particle swarm optimization Matrix converters Databases Data mining Genetic algorithms Heuristic algorithms

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Solving Linear Variation Inequality by Particle Swarm optimization

Solving Linear Variation Inequality by Particle Swarm Optimi...

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international workshop on Intelligent Systems and applications, ISA

作者： Liangdong Qu Dengxu He College of Mathematics and Computer Science Guangxi University for Nationalities Nanning China

ISBN: (纸本)9781424458721;9781424458745

Solving linear variation inequality by traditional numerical iterative algorithm can not satisfy parallel. In this paper, particle swarm optimization is used to solve linear variation inequality, which sufficiently exerts the advantage of particle swarm optimization such as group search and global convergence and it satisfies the question of parallel solving linear variation inequality in engineering. Several numerical simulation results show that the algorithm offers an effective way to solve linear variation inequality, high convergence rate, high accuracy and robustness.

关键词： Particle swarm optimization Educational institutions Iterative algorithms Vectors Iterative methods Helium Mathematics Computer science Convergence Numerical simulation

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An Improved Ant Colony Algorithm for Path optimization in Emergency Rescue

An Improved Ant Colony Algorithm for Path Optimization in Em...

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international workshop on Intelligent Systems and applications, ISA

作者： Baohua Jin Lei Zhang School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9781424458721;9781424458745

The ant colony algorithm is widely applied to optimize the complex problems in many fields with its features of being robust, parallel, flexible, demanding no artificial interference, and accurate. This paper discusses the application of the colony algorithm in the path search of the earthquake emergency rescue. We first construct a mathematical model for emergency rescue based on the earthquake disasters. Then we propose an improved ant colony algorithm for the rescue path searching optimization according to the specific characteristics of the mode, and the global iterative update strategy is used with limiting the rescue entries. Experiments show that the proposed algorithm can overcome the shortcomings of conventional algorithms such as slow convergence, easily trapped in local optimum, and demonstrating high and flexibility performance.

关键词： Ant colony optimization Earthquakes Iterative algorithms Roads Robustness Hazards Feedback Distributed computing Concurrent computing Communication industry

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The Research on the Matching Design of the Ship-Engine-Propeller Based on Multi-Objective Particle Swarm optimization

The Research on the Matching Design of the Ship-Engine-Prope...

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international workshop on Intelligent Systems and applications, ISA

作者： Aihua Liu Shidong Fan Sch. of Energy & Power Eng. Wuhan Univ. of Technol. Wuhan China

The matching design problem of the ship-engine-propeller is a non-linear constrained multi-objective optimization problem which is performed based on multiple objectives,such as system efficiency and the life cycle cost. A multi-objective particle swarm optimization (MOPSO) approach for matching design problem of the ship-engine-propeller problem is presented in this paper.A real matching design problem of the ship-engine-propeller is employed to demonstrate applicability of the proposed *** results indicate that multi-objective particle swarm optimization approach in comparison with most of multi-objective optimization algorithms such as the multiple objective genetic algorithm (MOGA) is simple ,efficient and operable.

关键词： Particle swarm optimization Marine vehicles Design optimization Propellers Algorithm design and analysis Engines Cost function Constraint optimization Fuels Power engineering and energy

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Traffic Intersection Signal-Planning Multi-Object optimization Based on Genetic Algorithm

Traffic Intersection Signal-Planning Multi-Object Optimizati...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Ben Zhang Lei Shang Dan Chen Sch. of Energy & Power Eng. WuHan Univ. of Technol. Wuhan China Marine Eng. Inst. JiMei Univ. Xiamen China

For improving the intersection traffic capacity and reducing the vehicle emission, the solution that aim at the multi-object optimization was presented by using genetic algorithm (GA), and urban traffic microscopic simulation model (UTMSM) combined with GA was developed. The simulation was performed and the result indicated that the optimization method in this paper was effective to get better traffic signal control effects and to improve the environment.

关键词： Genetic algorithms Traffic control Vehicles optimization methods Delay effects Biological system modeling Acceleration Power engineering and energy Microscopy Automotive engineering

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optimization of SVM MultiClass by Particle Swarm (PSO-SVM)

Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)

引用

international workshop on Database Technology and applications

作者： Fatima Ardjani Kaddour Sadouni Mohamed Benyettou Computer Science Department Laboratory LAMOSI University of Sciences and Technology-Mohamed Boudiaf Oran Algeria

In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor. Among the methods of optimization used we chose the method PSO (Particle Swarm optimization) which makes it possible to optimize the performance of classifier SVM (Separating with Vast Margin). The experiments are carried out on corpus TIMIT. The results obtained show that approach PSO-SVM gives a better classification in terms of accuracy even though the execution time is increased.

关键词： Kernel Particle swarm optimization Classification algorithms Training Support vector machine classification Accuracy

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Model-driven development with optimization of non-functional constraints in sensor network

Model-driven development with optimization of non-functional...

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2010 ICSE workshop on Software Engineering for Sensor Network applications, SESENA 2010, in Conjunction with the 32nd ACM/IEEE international Conference on Software Engineering, ICSE 2010

作者： Thang, Nguyen Xuan Geihs, Kurt Distributed Systems Group University of Kassel Wilhelmshöher Allee 73 34121 Kassel Germany

ISBN: (纸本)9781605589695

Current methods for sensor network programming lead developers to cope with not only high-level concerns such as application logic, quality of service, adaptability, reliability, but also low-level mechanism like ad-hoc routing and communication, resource management, data filtering, and aggregation. This makes the development of software expensive and error-prone, even for expert programmers. Model-driven development (MDD) allows designers to model their systems at different abstraction levels and thus reduces the complexity of the development task. However MDD does not help system architects to solve the problem of optimizing the trade-off between different constraints such as memory usage, latency, and power consumption in the application design process. This paper proposes an approach to address this issue by combining MDD with Evolutionary algorithms (EA). In our approach various metamodels of the system to be developed are generated and evolved, the optimal model is selected in terms of evaluating the trade-off between different constraint criteria and performance value. © 2010 ACM.

关键词： Sensor networks

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