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检索条件"主题词=genetic algorithm"

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Time Optimal Missile Launching Plan for MLV based on Improved GA and AHP

Time Optimal Missile Launching Plan for MLV based on Improve...

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第30届中国控制与决策会议

作者： Da Wang Xuefeng Zhou Zhihao Xu Taobo Cheng Shenyang University of Technology School of information science and engineering Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing

Missile launching by vehicle have some inadequate aspects, such as the lack of rapid deployment for combat troops, demand of contingency plans to deal with temporary mutations, and the inability to protect prominent road nodes. In this paper, fire assignment model according with actual battlefield is built. According to the characteristics of missile launching vehicle（MLV）, a dynamic fire distribution model of the whole launch time is established. The missile launching scheme is formulated by using improved genetic algorithm coding method, combining tabu list and Dijkstra algorithm. Risk assessment system of road nodes is built by analytic hierarchy process（AHP）, and corresponding protection mechanism is established. Simulation results show that the method can increase the stability, expand the search range and analyze the hazardous nodes of the solution, verifying the feasibility and effectiveness of the model and algorithm. The overall launch time is short, and the unsafe node is protected.

关键词： Missile Launching Vehicle genetic algorithm Analytic Hierarchy Process Fire Assignment

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Constrained Silhouette Based Evolutionary K-Means

Constrained Silhouette Based Evolutionary K-Means

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2013年中国智能自动化学术会议

作者： Zhenfeng He College of Mathematics and Computer Science Fuzhou University

Evolutionary K-Means(EKM) is a non-parametric approach proposed to improve K-Means algorithm.Current EKM approaches are ineffective in deciding the correct cluster number of real datasets.This paper uses instance-level constraints to solve this problem and presents a Constrained Silhouette(CS) based algorithm,namely CS-EAC.Firstly CS is defined to combine constraints into the computation of Silhouette Information(SI).Updated from the Fast Evolutionary algorithm for Clustering algorithm(F-EAC),CS-EAC uses CS instead of SI to guide the genetic operations.Experimental results suggest that CS-EAC is effective in both deciding the correct number of clusters and improving the accuracy of clustering for real datasets.

关键词： Clustering Semi-supervised clustering genetic algorithm Silhouette information

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Application of a Machine Learning algorithm for the Structural Optimization of Circular Arches with Different Cross-Sections

Application of a Machine Learning Algorithm for the Structur...

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作者： Jonathan Melchiorre Amedeo Manuello Bertetto Giuseppe Carlo Marano Department of Structural Building and Geotechnics Politecnico di Torino.Corso Duca degli Abruzzi

Arches are employed for bridges. This particular type of structures, characterized by a very old use tradition, is nowadays, widely exploited because of its strength, resilience, cost-effectiveness and charm. In recent years, a more conscious design approach that focuses on a more proper use of the building materials combined with the increasing of the computational capability of the modern computers, has led the research in the civil engineering field to the study of optimization algorithms applications aimed at the definition of the best design parameters. In this paper, a differential formulation and a MATLAB code for the calculation of the internal stresses in the arch structure are proposed. Then, the application of a machine learning algorithm, the genetic algorithm, for the calculation of the geometrical parameters, that allows to minimize the quantity of material that constitute the arch structures, is implemented. In this phase, the method used to calculate the stresses has been considered as a constraint function to reduce the range of the solutions to the only ones able to bear the design loads with the smallest volume. In particular, some case studies with different cross-sections are reported to prove the validity of the method and to compare the obtained results in terms of optimization effectiveness.

关键词： Structural Optimization genetic algorithm Boundary Value Problem Arch Volume Minimization

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Intelligent Welding Robot Path Planning

Intelligent Welding Robot Path Planning

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2015年中国智能自动化学术会议

作者： Xue Wu Wang Ying Pan Shi Rui Yu Xing Sheng Gu School of Information Science and Engineering East China University of Science and Technology

Spot welding robots are now widely used in manufacturing industry,and usually many welding joints have to be traversed in welding process.The path planning for welding robot is based on engineering experiments where teaching and playback were applied in most cases.It usually takes the engineer much time to obtain desired welding path,and sometimes,it is difficult to find an optimal path for spot welding robot especially when the number of welding joints is huge.Hence,welding robot path planning has become one key technology in this field.Intelligent optimization algorithm is beneficial for realizing effective welding robot path planning.To this end,particle swarm optimization(PSO) algorithm was improved first.Then,the improved PSO algorithm was applied for path planning of welding robot,and the simulation results show the effectiveness of the method.

关键词： Welding robot Path planning Particle swarm optimization genetic algorithm

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Dynamic Traffic Assignment and Signal Control in Saturated Road Networks

Dynamic Traffic Assignment and Signal Control in Saturated R...

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2005全国博士生学术论坛（交通运输工程学科）

作者： Lian Aiping, Ren Hualing, Gao Ziyou (School of traffic and transportation, Beijing Jiaotong University, Beijing 100044)

＜正＞ This paper presents a generalized bi- level programming model fcr dynamic traffic assignment and traffic signal central in saturated road networks, and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO （Fust -In-First -Out） rule. The genetic algorithm （GA） proposed in this paper can not only present the optimal signal settings, but also calculate, at each interval, the link inflow rates, the link outflow rates for the dynamic user optimal problem, and provide the real-time information for the travelers. Finally, a numerical example is given to illustrate the applications of the proposed model and solution algorithm, and comparison shows that this model has better system performance.

关键词： dynamic user optimal traffic optimal signal setting genetic algorithm

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A Molecular Docking Approach Study on Ant Colony Optimization algorithm

A Molecular Docking Approach Study on Ant Colony Optimizatio...

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2007中国生物医学工程联合学术年会

作者：常珊北京工业大学

＜正＞Molecular docking technology is a powerful approach to explore molecular interactions and recognition. In this work, the Ant Colony Optimization （ACO） method was applied in docking program and the new docking programs were applied to dock the small molecule XK263 to its target HIV-1 protease. The results show that the ACO method can make a better performance than the genetic algorithm （GA） in the docking process.

关键词： Molecular Docking Ant Colony Optimization genetic algorithm

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A Fuzzy Programming Approach for Supply Chain Network Design

A Fuzzy Programming Approach for Supply Chain Network Design

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第四届中国不确定系统年会

作者： Ji Xiaoyu School of Business, Renmin University of China, Beijing, 100872, China

This paper presents a fuzzy programming method to design supply chain network, in which the customer demands and transportation costs are assumed to be fuzzy parameters. Existing researches on these problems are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider supply chain network design problem in fuzzy environment. Under different criteria, we format three types of models for the decision makers: expected cost optimization model, chance-constrained model and chance maximization model. A genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models. Moreover, some numerical examples are presented to illustrate the effectiveness of models and solution algorithm.

关键词： Supply chain design fuzzy programming genetic algorithm fuzzy simulation

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Portfolio Selection with Fuzzy Returns

Portfolio Selection with Fuzzy Returns

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第二届不确定系统年会

作者： Xiaoxia Huang School of Management, University of Science and Technology Beijing Beijing 100083, China

Portfolio selection is to select a group of securities which can best meet the objectives of the investors. It starts with returns of individual securities and ends with conclusions concerning portfolios as a whole. Returns of individual securities are usually considered as random variables and various models are given based on probability theory and optimization theory. Though probability theory helps a lot in selecting efficient portfolios, it is not the only tool. In some circumstances when probability distributions of returns of individual securities cannot be obtained, fuzzy set theory is a good alternative. This paper considers portfolio selection with fuzzy returns. Two new mean-variance models are given. Fuzzy simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm to solve the models and a numerical example is presented to illustrate the proposed approach.

关键词： portfolio selection fuzzy set genetic algorithm

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Optimization of machining feature sequencing based on bi-level directed acyclic graphs

Optimization of machining feature sequencing based on bi-lev...

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第40届中国控制会议

作者： Haoyuan Xia Chaochen Gu Chenyue Zhang Lin Wang Department of Automation Shanghai Jiao Tong University

The major challenge of machining feature（MF） sequencing and setup partitioning is how to minimize processing cost while satisfying various kinds of constraints including precedence constraints and joint constraints. In this paper, a bi-level directed acyclic graph is developed to deal with different kinds of constraints, which can be applied to large-scale optimization problems with huge constraints. Moreover, the classic heuristic algorithm genetic algorithm and forward topological sorting algorithm are combined together to optimize the machining feature sequencing and setup partitioning. Finally, large-scale data experiments are used to test the feasibility of the algorithm.

关键词： Machining Feature Sequencing Directed Acyclic Graph Forward Topological Sorting genetic algorithm

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Fuzzy Multi-product EOQ Model with Imperfect Quality and Shortages

Fuzzy Multi-product EOQ Model with Imperfect Quality and Sho...

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The Eighth International Conference on Information and Management Sciences

作者： Mohuya B.Kar Samarjit Kar Department of Computer Science and Engineering Heritage Institute of TechnologyKolkata-700 107India Department of Mathematics National Institute of TechnologyDurgapur 713 209India

＜正＞In this study,we develop a multi-product economic order quantity（EOQ） model with unreliable supply characterising by a random fraction of imperfect quality items and a screening process under budget constraint.Shortages are allowed and backlogged completely.100%screening process is performed and the items of imperfect quality are sold as a single batch at a discounted price,before receiving the next lot.The demands of the products are assumed to be imprecise and represented by a fuzzy number.According to the decision-maker’s targeted goals;three types of fuzzy models have been developed to solve the proposed model.Moreover,the hybrid intelligent algorithm based on genetic algorithm（GA） and fuzzy simulation is designed to solve the fuzzy models. Finally,a numerical example has been provided for the evaluation and validation of these models and algorithm.

关键词： EOQ model Imperfect quality Complete backordering Fuzzy programming Fuzzy simulation genetic algorithm

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