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Research on hierarchical genetic algorithm Optimized Based on Fuzzy Neural Network 1

International Conference on Applied Informatics and Communication (ICAIC 2011)

作者： Hao, Yuan Ren, Zhaohui Wang, Bingcheng Northeastern Univ Sch Mech Engn & Automat Shenyang 110004 Liaoning Provin Peoples R China Shenzhen Univ Sch Mech Engn Shenzhen 518040 Peoples R China

ISBN: (数字)9783642232206

ISBN: (纸本)9783642232190

A new algorithm of fuzzy neural network learning is presented. It is based on combining genetic algorithm of hierarchical structure with evolution programming. This algorithm is used to optimize the structure and parameters of fuzzy neural network, reject redundant nodes and redundancy connections, and improve the treatment ability of the network. The results of analysis and experiment show that, by using this method the fuzzy neural network of mechanical fault diagnosis has good concise structure and diagnosis effect.

关键词： Fuzzy neural network hierarchical genetic algorithm Evolutionary programming

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An improved hierarchical genetic algorithm for collaborative optimization of manufacturing processes in metal structure manufacturing systems

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ADVANCES IN MECHANICAL ENGINEERING 2017年第3期9卷

作者： Qi, Dezhong Zhang, Sanqiang Liu, Mingyong Lei, Yakuo Hubei Univ Technol Hubei Agr Machinery Engn Res & Design Inst Wuhan 430068 Peoples R China

The multi-objective optimization problem includes plate nesting, production planning, scheduling, and equipment capacity optimization in the complex manufacturing process of metal structures. For the best optimization results, a global collaborative optimization of the manufacturing system is necessary. A multi-objective optimization model for optimized nesting, optimized scheduling, dispatch optimizing, and equipment load balancing is constructed, and an improved hierarchical genetic algorithm is then developed for a better solution. A hierarchical structure of three chromosomes is designed in this algorithm. The algorithm can be used to simultaneously solve the layout selection, process sequencing, and machine selection problems. The algorithm shortens the production cycle, reduces the number of work in process, and improves equipment utilization through the application of collaborative optimization. The computational result and comparison prove that the presented approach is quite effective to address the considered problem.

关键词： Metal structures collaborative manufacturing hierarchical genetic algorithm cutting collaborative optimization

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OPTIMIZING A FUZZY NEURAL NETWORK WITH A hierarchical genetic algorithm

OPTIMIZING A FUZZY NEURAL NETWORK WITH A HIERARCHICAL GENETI...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： MING MA LI-BIAO ZHANG Information Manage Center Beihua University Jilin 132013 China College of Computer Science and Technology Jilin University Changchun 130012 China

We have proposed an algorithm to optimize fuzzy neural network based on hierarchical genetic *** can evolve both the fuzzy neural network's topology and weighting *** a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the *** simulations show the effectiveness of the proposed algorithm.

关键词： hierarchical genetic algorithm Fuzzy neural network Optimize

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Power system stabilizer tuning based on multiobjective design using hierarchical and parallel micro genetic algorithm

Power system stabilizer tuning based on multiobjective desig...

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International Conference on Power System Technology (POWERCON 2004)

作者： Hongesombut, K Mitani, Y Dechanupaprittha, S Ngamroo, I Pasupa, K Tippayachai, J Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)0780386108

In order to achieve the optimal design based on some specific criteria by applying conventional techniques, sequence of design, selected location of PSSs are critical involved factors. This paper presents a method to simultaneously tune PSSs in multimachine power system using hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) based on multiobjective function comprising the damping ratio, damping factor and number of PSSs. First, the problem of selecting proper PSS parameters is converted to a simple multiobjective optimization problem. Then, the problem will be solved by a parallel micro GA based on HGA. The stabilizers are tuned to simultaneously shift the lightly damped and undamped oscillation modes to a specific stable zone in the s-plane and to self identify the appropriate choice of PSS locations by using eigenvalue-based multiobjective function. Many scenarios with different operating conditions have been included in the process of simultaneous tuning so as to guarantee the robustness and their performance. A 68-bus and 16-generator power system has been employed to validate the effectiveness of the proposed tuning method.

关键词： hierarchical genetic algorithm multiobjective design parallel micro genetic algorithm power system stabilizer tuning

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Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 27卷 41-56页

作者： Sanchez, Daniela Melin, Patricia Tijuana Inst Technol Tijuana Mexico

A new model of a modular neural network (MNN) using a granular approach and its optimization with hierarchical genetic algorithms is proposed in this paper. This model can be used in different areas of application, such as human recognition and time series prediction. In this paper, the proposed model is tested for human recognition based on the ear biometric measure. A benchmark database of the ear biometric measure is used to illustrate the advantages of the proposed model over existing approaches in the literature. The proposed method consists in the optimization of the design parameters of a modular neural network, such as number of modules, percentage of data for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons. This method also finds out the amount of and the specific data that can be used for the training phase based on the complexity of the problem. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Modular neural networks Granular computing hierarchical genetic algorithm Human recognition

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A hierarchical genetic algorithmfor hardware software co-synthesis with a Stochastic approach

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IETE JOURNAL OF RESEARCH 2002年第5期48卷 349-360页

作者： Chakraverty, S Ravikumar, CP Choudhuri, DR Netaji Subhas Inst Technol New Delhi 110045 India Texas Instruments India Bangalore 560017 Karnataka India Delhi Coll Engn Delhi 110042 India Indian Inst Technol Dept Elect Engn Delhi India

One of the most challenging problems that occurs in the design of real-time embedded systems is to take into account the stochastic nature of different timing parameters which affect the system performance. In this paper we propose a stochastic framework for hardware-software co-synthesis whereby the task execution times, the data communication times and the input arrival. times are all assumed to be random variables. Based on this framework, a hierarchical genetic algorithm has been developed which explores the state space of possible architectures and pro duces a set of evolved ones which are optimized with respect to cost and performance. This is coupled with a stochastic scheduling algorithm which generates high performance stochastic schedules for the tasks and estimates the overall deadline meeting probability of the system.

关键词： real time embedded systems hardware-software co-synthesis task precedence graph Stochastic scheduling hierarchical genetic algorithm

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A novel clustering approach using hierarchical genetic algorithms

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2005年第3期11卷 143-153页

作者： Lai, CC Natl Univ Tainan Dept Comp Sci & Informat Engn Tainan 700 Taiwan

Clustering is a central task for data analysis that partitions heterogeneous data sets into groups of more homogeneous characteristics. However, most of clustering algorithms require the user to provide the number of clusters as input. In this paper, we consider the automatic clustering problem that one has to partition data points without any a priori knowledge about the correct number of clusters. The hierarchical genetic algorithm (HGA) is employed for automatically searching the number of clusters as well as properly locating the centers for clusters. The well-known Davies-Bouldin index is adopted as a measure of the validity of the clusters. Experimental results on artificial and real-life data sets are given to illustrate the effectiveness of the proposed approach.

关键词： clustering hierarchical genetic algorithm Davies-Bouldin index

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Optimization of Yagi array by hierarchical genetic algorithms

Optimization of Yagi array by hierarchical genetic algorithm...

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IEEE Radio and Wireless Conference (RAWCON 2003)

作者： Wang, HJ Man, KF Chan, CH Luk, KM Chinese Acad Sci Natl Microwave Remote Sensing Lab Beijing Peoples R China

ISBN: (纸本)0780378296

A method of hierarchical genetic algorithms (HGA) is used for optimizing the element spacing and lengths of Yagi-uda antennas. This scheme has the ability of handling multiobjective functions as well as the discrete constraints in the numerical optimizing process. Together with the technique of Pareto ranking scheme, more than one possible solution can be obtained. It has been found that the number of dipoles of the antenna can be minimally identified with multi-facet design criteria as well as the imposed stringent constraints on the antenna design. Furthermore, This added feature also enables a design tradeoff between cost and performance without extra computational effort.

关键词： antenna array hierarchical genetic algorithm multiobjective optimization

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Control strategy for multi-objective coordinate voltage control using hierarchical genetic algorithms

Control strategy for multi-objective coordinate voltage cont...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Ma, H. M. Man, K. F. Hill, D. J. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780394836

hierarchical genetic algorithm (HGA) is proposed for optimizing the power voltage control systems according to number of control actions. The advantage of HGA is its capability in control the parametric genes of chromosome. In this paper, we apply HGA to find out the optimal solution for coordinate voltage control in a simple six buses power system. The number of control actions is fixed from one to six by HGA. Because of the multi-objective classification of the obtained solutions, all these solutions could therefore form a landscape of control pattern which is aptly applicable to the control purpose of coordinate power control system. The application of the proposed paradigm is demonstrated through simulation and the results obtained suggested that the speed of voltage recovery in some degree related with the number of actions of control when emergency happened. The effective of control is influenced by the location of control devices and the system structure.

关键词： hierarchical genetic algorithm coordinate voltage control multi-objective optimization Pareto front

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Optimization design based on hierarchic genetic algorithm and particles swarm algorithm

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2018年第3期12卷 217-222页

作者： Lin DengWei Jiaozuo Univ Fac Informat Engn Jiaozuo 454003 Peoples R China

For a lot of data, it is time-consuming and unpractical to get the best combination by manual tests. The genetic algorithm can make up for this shortcoming through the optimization of parameters. In this paper, the advantages of traditional similarity algorithm is studied, the time model and the trust model for further filtering are introduced, and the parameters with the combination of hierarchical genetic algorithm and particle swarm algorithm are optimized. In the collaborative filtering algorithm, genetic algorithm is improved with hierarchical algorithm, and the user model and the algorithm process are optimized using the fitness function of selection, crossover, and variation, along with the optimization of recommendation result set. In the algorithm, the global optimal parameters can be calculated with the optimization of the obtained initial data, and the accuracy of the similarity calculation can also be improved. This study does the recommendation and comparison experiment in the MovieLens Dataset, and the results show that, on the basis of obtaining the nearest neighbor user group, the mixing use of the hierarchical genetic algorithm and the particle swarm algorithm can make more improvement in the recommendation quality than that of the traditional similarity algorithm.

关键词： hierarchical genetic algorithm particle swarm algorithm similarity optimization algorithm resource recommendation

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