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 selectionalgorithm, which is de...
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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 selectionalgorithm, which is derived as a special selectionalgorithm 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
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 (CLONA...
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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.
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...
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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.
作者：
Wang, XAalto Univ
Inst Intelligent Power Elect FIN-02150 Espoo Finland
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 opti...
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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.
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...
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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.
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....
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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.
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...
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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.
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, clonalselection algorith...
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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.
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 cons...
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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.
At present, many cloning selectionalgorithms 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 o...
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At present, many cloning selectionalgorithms 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.
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