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intelligent genetic algorithm with a gradient-based local search applied to supersonic wing planform optimization

JOURNAL OF MECHANICS 2007年第4期23卷 285-293页

作者： Liu, J. -L. Chen, J. -L. Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung 80424 Taiwan I Shou Univ Dept Mech & Automat Engn Kaohsiung 84001 Taiwan

This study proposes an efficiently evolutionary algorithm, termed IGA-LS, which combines an intelligent genetic algorithm (IGA) with a gradient-based local search (LS). The IGA performs a crossover operation and uses a fractional factorial design to determine the optimal combination of design variables. That is, in the proposed IGA, the chromosomes of children are generated via an intelligent crossover process with factorial experiments after the execution of the selection operation of the GA. This gene mating process differs from that of the traditional GA, in which each chromosome of child exchange genes randomly. The gradient-based optimization approach seeks feasible and usable directions in which to minimize a specified objective function. The initial conditions are provided by the IGA in each generation. Therefore, the IGA-LS offers fast convergence and high numerical accuracy. Several multi-modal test functions are introduced herein to examine the algorithmic capacity and efficiency of finding the global optima. Moreover, the proposed IGA-LS algorithm is applied to optimize the design of a supersonic wing planform for supersonic airplane. The objective function is to minimize the drag during supersonic cruising. From the numerical results, the presented algorithm outperforms the traditional GA, the micro-GA and the intelligent GA in terms of convergence rate and value of the optimal function.

关键词： intelligent genetic algorithm gradient-based local search supersonic wing planform

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Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

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IET RENEWABLE POWER GENERATION 2016年第8期10卷 1220-1228页

作者： Civelek, Zafer Cam, Ertugrul Luy, Murat Mamur, Hayati Cankiri Karatekin Univ Tech Business Coll Cankiri Turkey Kirikkale Univ Fac Engn Kirikkale Turkey Cankiri Karatekin Univ Fac Engn Cankiri Turkey

Output powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional-integral-derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.

关键词： three-term control genetic algorithms wind turbines power generation control proportional-integral-derivative parameter optimisation blade pitch controller wind turbines intelligent genetic algorithm PID controllers hot systems community-based optimisation methods PID parameter setting optimisation IGA controllers WT mutation rate MATLAB Simulink software

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Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

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PATTERN RECOGNITION LETTERS 2002年第13期23卷 1495-1503页

作者： Ho, SY Liu, CC Liu, S Feng Chia Univ Dept Informat Engn Taichung 407 Taiwan

The goal of designing an optimal nearest neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA) superior to conventional GAs in solving large parameter optimization problems is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and total number of parameters of the reduced sets. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： feature selection intelligent genetic algorithm minimum reference set nearest neighbor classifier

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Parameter determination for a rail system simulator by an intelligent genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT 2004年第2期218卷 149-157页

作者： Ruan, W Giras, TC Lin, Z On, Y Univ Virginia Dept Elect & Comp Engn Ctr Rail Safety Crit Excellence Charlottesville VA 22904 USA

This paper reports on a successful determination of the train travel schedule parameters for a rail system based on limited data, and thus provides a verification of the axiomatic safety-critical assessment process (ASCAP), a rail system simulator developed at the Center of Rail Safety-Critical Excellence at the University of Virginia. The train system considered is a corridor encompassing a territory of over 127 miles. It is divided into 37 train speed zones, with nine sidings. The only data available are the actual trip times of 171 trains dispatched over a period of 14 days. The problem of determining the 37 train-speed-zone average speeds and nine siding delay times was formulated as a constrained optimization problem. The cost to be minimized is the accumulated errors between the actual train trip times and the ASCAP simulated trip times resulting from a particular set of train-speed-zone average speeds and siding delay times. The constraints include allowable siding delays, permissible train-speed-zone average speeds and prohibition of southbound trains from entering the sidings. This large-scale non-linear optimization problem was then solved by a genetic algorithm developed by the present authors and referred to as the intelligent genetic algorithm. Simulation results demonstrate the effectiveness of this intelligent genetic algorithm.

关键词： rail system simulator genetic algorithm intelligent genetic algorithm axiomatic safety-critical assessment process (ASCAP)

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Infrared image segmentation based on 2-D maximum fuzzy entropy and intelligent genetic algorithm

Infrared image segmentation based on 2-D maximum fuzzy entro...

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International Conference on intelligent Mechatronics and Automation

作者： Wu, J Li, J Liu, J Tian, JW Wuhan Univ Sci & Technol Coll Informat Sci & Engn Wuhan 430081 Hubei Province Peoples R China

ISBN: (纸本)0780387481

This paper presents a fast and effective segmtentation method for Forward Looking infra-Red (FLIR) image, based on fuzzy filtering, the principle of 2-D maximum fuzzy a entropy and intelligent genetic algorithm. A new fuzzy operator is applied to the enhancement of noisy images. Basing on fuzzy relation and maximum fuzzy entropy principle, we perform fuzzy partition on a 2-D histogram. A new intelligent genetic algorithm (IGA), which applies an intelligent crossover (IC) based on orthogonal arrays (OAS), is proposed to search the optimal combination of the fuzzy parameters finally used to segment the image. Experiment results show that the proposed method can depress the Gaussian (-like) noise effectively, segment the infrared image automatically and properly, and is faster than the conventional genetic algorithm and exhaustive search.

关键词： infrared image segmentation fuzzy filtering fuzzy entropy membership function intelligent genetic algorithm

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Infrared image segmentation via intelligent genetic algorithm based on maximum entropy

Infrared image segmentation via intelligent genetic algorith...

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7th International Conference on Signal Processing

作者： Wu, J Qiu, Y Liu, J Tian, JW Huazhong Univ Sci & Technol State Key Lab Image Proc & Intelligene Control Wuhan 430074 Peoples R China

ISBN: (纸本)7121002159

This paper presents a fast and effective segmentation method for infrared image, based on fuzzy filtering, the criteria of maximum entropy and intelligent genetic algorithm. A fuzzy filter is applied to depress the Gaussian(-like) noise. Then we use the theory of maximum entropy to select the optimum threshold. A new intelligent genetic algorithm (IGA), which applies an intelligent crossover (IC) based on orthogonal arrays (OAS) is proposed to solve this optimal problem. Experiment results show that the proposed method can depress the Gaussian(-like) noise effectively, segment the infrared image properly, and is faster than the conventional genetic algorithm and Exhaustive search, also is easier to implement on hardware.

关键词： infrared image segmentation fuzzy filtering maximum entropy intelligent genetic algorithm thresholding

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Early warning and detection of geological disasters based on intelligent genetic algorithm

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Sun, Dan Zhou, ZhiMin Liao, ZhiWu Aba Teachers Univ Sch Comp Sci & Technol Aba 623002 Peoples R China Aba Teachers Univ Sch Resources & Environm Aba 623002 Peoples R China Sichuan Normal Univ Sch Comp Sci Chengdu 610101 Peoples R China

In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production and life, which not only causes serious damage to infrastructure, but also creates a certain degree of fear for people. Geological disaster is an open nonlinear complex system, which has extraordinary complex geological process, formation conditions, and causes. Therefore, it makes difficulty in capturing the dynamic information and searching for the global optimal solution. Meanwhile, traditional geological disaster warning system has the deficiencies of single disaster warning and low accuracy. In order to improve the level of early warning and detection of geological disasters, this paper combined the genetic algorithm with superior performance and Support Vector Regression (SVR) algorithm to establish a feasible and credible early warning and monitoring model for geological disasters. The experimental results show that the early warning and monitoring model proposed in this paper can greatly improve the ability of geological disaster prevention and early warning, and greatly improve the level of disaster prevention and early warning, with good engineering application value.

关键词： intelligent genetic algorithm Geological disaster Early warning and detection SVR Engineering application

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Solving cross-matching puzzles using intelligent genetic algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2018年第2期49卷 211-225页

作者： Kesemen, Orhan Ozkul, Eda Karadeniz Tech Univ Dept Stat & Comp Sci Fac Sci TR-61080 Trabzon Turkey

Cross-matching puzzles are logic based games being played with numbers, letters or symbols that present combinational problems. A cross-matching puzzle consists of three tables: solution table, detection table, and control table. The puzzle can be solved by superposing the detection and control tables. For the solution of the cross-matching puzzle, a depth first search method can be used, but by expanding the size of the puzzle, computing time can be increased. Hence, the genetic algorithm, which is one of the most common optimization algorithms, was used to solve cross-matching puzzles. The multi-layer genetic algorithm was improved for the solution of cross-matching puzzles, but the results of the multi-layer genetic algorithm were not good enough because of the expanding size of the puzzle. Therefore, in this study, the genetic algorithm was improved in an intelligent way due to the structure of the puzzle. The obtained results showed that an intelligent genetic algorithm can be used to solve cross-matching puzzles.

关键词： genetic algorithm Depth first search Cross-matching puzzle Multi-layer genetic algorithm intelligent genetic algorithm

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Design of high performance fuzzy controllers using flexible parameterized membership functions and intelligent genetic algorithms

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JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING 2003年第1期46卷 252-262页

作者： Ho, SY Ho, SJ Chen, TK Feng Chia Univ Dept Informat Engn Taichung Taiwan Natl Huwei Inst Technol Dept Automat Engn Huwei Taiwan

This paper. proposes a method for designing high performance fuzzy controllers with a compact rule system. The method is mainly derived from flexible parameterized membership functions (FPMFs) and an intelligent genetic algorithm (IGA) which is superior to the traditional GAs in solving large parameter optimization problems. An FPMF consists of flexible trapezoidal fuzzy sets that the fuzzy set is encoded using five parameters. Furthermore, the membership functions and fuzzy rules are simultaneously determined by effectively encoding all the system parameters into chromosomes. Therefore, the optimal design of fuzzy controllers is formulated as a large parameter optimization problem, which can be effectively solved by IGA. The proposed method is demonstrated by two well-known problems, truck backing and cart centering problems. It is shown empirically that the performance of the proposed method is superior to those of existing methods in terms of the numbers of time steps and fuzzy rules.

关键词： fuzzy controller intelligent genetic algorithm linguistic modeling membership function orthogonal experimental design

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Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 1-12页

作者： Rajkumar, S. Katiravan, Jeevaa Velammal Engn Coll Chennai India

Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. intelligent genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized intelligent genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized intelligent genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized intelligent genetic Load Balancer system performance with existing simulations results and compared the results.

关键词： Enhanced load balancer Deep belief networks Virtual machine Cloud service provider intelligent genetic algorithm

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