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clonal selection algorithm APPLICATION TO SIMPLE MICROWAVE MATCHING NETWORK

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2011年第5期53卷 991-993页

作者： Deniz, Ezgi Ulker, Sadik Girne Amer Univ Dept Elect Engn Girne Turkey Girne Amer Univ Dept Comp Engn Girne Turkey

Artificial immune systems are classified as computational systems inspired by theoretical immunology and are mechanisms that can solve complex problems. clonal selection algorithm is a selection algorithm, which is derived as a special selection algorithm to this artificial intelligence concept. In this work, clonal selection algorithm is applied to design a simple microwave matching network for a load with arbirtary impedance. The results obtained were tested in a microwave simulator and indicated that clonal selection algorithm can be very effectively applied for microwave design problems. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:991-993, 2011;View this article online at ***. DOI 10.1002/mop.25935

关键词： artificial immune systems clonal selection algorithm microwave matching network single shunt stub tuning

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clonal selection algorithm for Optimum Sizing of WT/PV/FC Hybrid Renewable Power System 1

Clonal Selection Algorithm for Optimum Sizing of WT/PV/FC Hy...

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1st International Conference on Computer Applications and Information Security (ICCAIS)

作者： Hatata, A. Y. Lafi, Alnufaie Shaqra Univ Dept Elect Engn Engn Collage Shaqraa Saudi Arabia

ISBN: (纸本)9781538644270

This paper obtains the optimum size of wind turbine/PV/fuel cell hybrid renewable power system (HRPS) using both Artificial Immune System (AIS) and HOMER software. The AIS is based on clonal selection algorithm (clonalG). The optimum size of the hybrid system will achieve two goals;satisfy the load demand with least loss of power supply probability and have to minimize the overall cost of the whole system. The cost of the optimum hybrid system includes initial, replacement, operating and maintenance cost (O&M) and salvage cost which are calculated during the whole lifetime of the project considering a discount factor. The hybrid system is determined to feed an electrical load at located at Qena AL-Gadeda City in Egypt using its predicted solar irradiation and wind data.

关键词： hybrid renewable power system PV Wind turbine fuel cell HOMER software clonal selection algorithm loss of power supply probability

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clonal selection algorithm with Adaptive Levy Mutation Operator 16

Clonal Selection Algorithm with Adaptive Levy Mutation Opera...

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1st International Conference on Informatics and Analytics (ICIA)

作者： Megala, T. Kashyap, Moganti Vivekanandan, K. Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry 605014 India

ISBN: (纸本)9781450347563

This paper studies clonal selection algorithm with different hypermutation operator such as Gaussian, Cauchy and Levy Mutation operator and employs an adaptive Levy hypermutation operator for solving complex numerical optimization problem. Levy mutation operator is based on Levy probability distribution which is capable of generating individuals far away from its parents. The proposed adaptive Levy hypermutation operator is applied to a set of benchmark functions. From the empirical evidence it is inferred that the proposed hypermutation operator performs better even for functions with many local optima than the Gaussian and Cauchy mutation operator.

关键词： Gaussian Mutation Cauchy Mutation Levy Mutation clonal selection algorithm

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clonal selection algorithm in power filter optimization

Clonal selection algorithm in power filter optimization

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IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications

作者： Wang, X Aalto Univ Inst Intelligent Power Elect FIN-02150 Espoo Finland

ISBN: (纸本)0780389425

Inspired by natural immune mechanisms, Artificial Immune Optimization (AIO) methods have been successfully applied to deal with numerous challenging optimization problems with superior performances over classical optimization techniques. clonal selection algorithm (CSA) is one of the most widely employed immune-based approaches for handling those optimization tasks. In this paper, the proposed CSA is used to search for the optimal parameters (values of inductor and capacitor) of a passive filter in the diode full-bridge rectifier. Simulation results demonstrate that the CSA-based approach can acquire the optimal LC parameters with certain given criteria for power filter design.

关键词： artificial immune system optimization clonal selection algorithm passive filter harmonic distortion

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clonal selection algorithm in Identification of Boundary Condition in the Inverse Stefan Problem 3

Clonal Selection Algorithm in Identification of Boundary Con...

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3rd International Conference on Man-Machine Interactions (ICMMI)

作者： Hetmaniok, Edyta Slota, Damian Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

ISBN: (纸本)9783319023090

In the paper a procedure for identifying the heat transfer coefficient in boundary condition of the inverse Stefan problem is presented. Stefan problem is a boundary value problem describing thermal processes with the change of phase and the proposed procedure is based on the clonal selection algorithm belonging to the group of immune algorithms.

关键词： artificial intelligence immune algorithm clonal selection algorithm inverse stefan problem

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clonal selection algorithm Based Optimization of the ADRC Parameters Designed to Control UAV Longitudinal Channel 5

Clonal Selection Algorithm Based Optimization of the ADRC Pa...

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5th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Huang, Yonghao Fei, Qing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781479982523

Active disturbance rejection controller has distinguished performance on restricting uncertain disturbances. However, setting ADRC parameters is a time consuming task and depends strongly on operator's experience. In this paper, we propose an approach for computing the ADRC parameters based on clonal selection algorithm (CSA). To validate this method, we build a MATLAB simulation for an UAV longitudinal channel control. Moreover, good results are obtained when we compare our approach to the one based on GA optimization. This work can be used to set parameters automatically, making the design of UAV controller more efficient.

关键词： ADRC clonal selection algorithm Parameter Optimization UAV control

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clonal selection algorithm for Image Compression

Clonal Selection Algorithm for Image Compression

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2010 IEEE World Congress on Computational Intelligence

作者： Liu, Ruochen Jiao, Licheng Zhang, Wei Ma, Jingjing Xidian Univ Inst Intelligent Informat Proc Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781424481262

Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, clonal selection algorithm for Image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

关键词： Linde-Buzo-Gray clonal selection algorithm data compression data compression data vectors encoding time image coding image compression mean-residual vector quantization modified k-means optimal codebook self-organising feature maps selforganizing mapping vector quantisation

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clonal selection algorithm for Feature selection and Parameters Optimization of Support Vector Machines

Clonal Selection Algorithm for Feature Selection and Paramet...

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2nd International Symposium on Knowledge Acquisition and Modeling

作者： Ding, Sheng Li, ShunXin Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Peoples R China

ISBN: (纸本)9780769538884

This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of Support Vector Machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to improve classification accuracy, the clonal selection algorithm and genetic algorithm are used to reach the optimization performances with several real-world datasets. The experiments show the effectiveness of the methods. And those results are compared each other. The experiments denote that the proposed clonal selection algorithm is shown to be an evolutionary strategy capable of improving the classification accuracy and has fewer features for support vector machines.

关键词： Artificial Immune System(AIS) Support Vector Machine(SVM) Feature selection clonal selection algorithm

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Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

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APPLIED SOFT COMPUTING 2015年 26卷 90-106页

作者： Galvez, Akemi Iglesias, Andres Avila, Andreina Otero, Cesar Arias, Ruben Manchado, Cristina Univ Cantabria Dept Appl Math & Computat Sci E-39005 Santander Spain Toho Univ Fac Sci Dept Informat Sci Funabashi Chiba 2748510 Japan Univ Cantabria Dept Geog Engn & Graph Express Tech E-39005 Santander Spain

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Reverse engineering B-spline curve fitting Knot adjustment Artificial immune systems clonal selection algorithm

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Improved clonal selection algorithm based on the directional update strategy

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19312-19331页

作者： Yang, Chao Huang, Zijing Jiang, Bibo Zhu, Menglian Luo, Aoran He, Jianfeng Hubei Univ Coll Comp & Informat Engn Youyi Ave Wuhan 430062 Hubei Province Peoples R China Hubei Prov Educ Informat Engn Technol Res Ctr Wuhan 430062 Hubei Province Peoples R China Social Sci Univ Hubei Prov Project Key Res Inst Humanities Res Ctr Informat Management Performance Evaluat Wuhan 430062 Hubei Province Peoples R China

At present, many cloning selection algorithms have been studied, and improvements have been made to the cloning, mutation and selection steps. However, there is a lack of research on the optimization of the updating operation steps. The clonal selection algorithm is traditionally updated through a random complement of antibodies, which is a blind and uncertain process. The added antibodies may gather near a local optimal solution, resulting in the need for more iterations to obtain the global optimal solution. To solve this problem, our improved algorithm introduces a crowding degree factor in the antibody updating stage to determine whether there is crowding between antibodies. By eliminating antibodies with high crowding potential and poor affinity, the improved algorithm guides the antibodies to update in the direction of the global optimal solution and ensures stable convergence with fewer iterations. Experimental results show that the overall performance of the improved algorithm is 1% higher than that of the clonal selection algorithm and 2.2% higher than that of the genetic algorithm, indicating that the improved algorithm is effective. The improved algorithm is also transplanted to other improved clonal selection algorithms, and the overall performance is improved by 0.97%, indicating that the improved algorithm can be a beneficial supplement to other improved clonal selection algorithms.

关键词： clonal selection algorithm Antibody update Targeted update strategy Antibody crowding Antibody affinity

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