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2011 International Conference on Advanced Materials in Microwaves and Optics（AMMO 2011）

作者： Farhad Samadzadegan Shahin Rahmatollahi Namin Mohammad Ali Rajabi Geomatics and Surveying Department University of Tehran

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection's higher performance to solve selection of features.

关键词： Component Artificial Immune Systems clonal selection algorithm Hyperspectral Image Image classification Immunos II Optimum feature selection

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Discussion of "application of excel solver for parameter estimation of the nonlinear Muskingum models" by Reza Barati

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第1期19卷 332-336页

作者： Vatankhah, Ali R. Univ Tehran Univ Coll Agr & Nat Resources Irrigat & Reclamat Eng Dept Karaj *** Iran

An accurate solution method is essential to the calibration of the nonlinear Muskingum model. Most of the earlier researchers have used inaccurate Euler's solution method which is manipulated to get a better fit for observed Wilson's data (1974). Euler's method which is adopted by most previous researchers is not very accurate and results in unsuitable simulation based on the nonlinear Muskingum model as shown in this discussion. This study proposes fourth-order Runge-Kutta method as a suitable and accurate solution method for simulation stage. When more accuracy is needed, the structure of the Muskingum model can be modified to produce more degree of freedom in model calibration procedure. For this purpose, a new five-parameter nonlinear Muskingum model is proposed. The proposed model is easy to formulate and use. The results show that the improvement in the fit of the proposed nonlinear Muskingum model is substantial.

关键词： Harmony Search Harmony Search algorithm clonal selection algorithm Hydrologic Engineer Muskingum Model

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A Modified Shuffled Frog Leaping algorithm for PAPR Reduction in OFDM Systems

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IEEE TRANSACTIONS ON BROADCASTING 2015年第4期61卷 698-709页

作者： Zhou, Jie Dutkiewicz, Eryk Liu, Ren Ping Huang, Xiaojing Fang, Gengfa Liu, Yuanan Macquarie Univ Dept Engn Sydney NSW 2109 Australia Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Commonwealth Sci & Ind Res Org ICT Ctr Marsfield NSW 2122 Australia Univ Technol Sydney NSW 2007 Australia

Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.

关键词： OFDM PAPR clonal selection algorithm shuffled frog leaping algorithm

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Research on SVM Plant Leaf Identification Method Based on CSA

Research on SVM Plant Leaf Identification Method Based on CS...

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The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

作者： Xuhui Zhang Yang Liu Haijun Lin Yukun Liu Harbin University of Science and Technology

In view of the longer training and recognition time of plant leaf classifier, this paper proposes a method of blade recognition based on the combination of clonal selection algorithm and support vector machine. The method uses the blade geometry and texture features as the identification feature,building a blade recognition classifier based on support vector machine, in order to obtain the optimal kernel function parameter and the penalty factor, using cross validation characteristics of immune clonal selection algorithm to optimize the kernel function parameter and the penalty factor. Experimental results show that compared with BP neural network and other two methods, the proposed method has a higher recognition accuracy and training speed.

关键词： Image identifying Support vector machine clonal selection algorithm Geometric features Texture features

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An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

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algorithmS 2015年第1期8卷 32-45页

作者： Yang, Dongdong Yang, Hui Fei, Rong Xian Univ Technol Inst Intelligent Comp & Image Proc Mail Box 6665 South JinHua Rd Xian 710048 Peoples R China China PLA Gen Polit Dept Engn Design & Res Inst Engineer Corps 4 Beijing 100850 Peoples R China

Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS) and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

关键词： principal component analysis clonal selection algorithm artificial immune system nonlocal mean filter SAR image segmentation

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Assessing the number of cells for a cell formation problem 15

Assessing the number of cells for a cell formation problem

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15th IFAC Symposium on Information Control Problems in Manufacturing

作者： Ulutas, Berna Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey

Cellular manufacturing design is concerned with the creation and operation of manufacturing cells to take the advantage of flexibility, efficient flow and high production rate. Cell formation problem (CFP) is the assignment of part types and machines to specific cells based on their similarity. Several exact and heuristic methods are provided in literature to solve the problem and test problems in literature are commonly used for comparison. This study presents a clonal selection algorithm (CSA) to solve a classical CFP that outperforms current available heuristics in the literature. The number of cells may be critical in the environments where cell formation costs are high and singletons occur in a design. It is concluded that the CFP results should be assessed not only based on efficacy values but also the number of cells. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： artificial immune systems cell formation problem clonal selection algorithm group technology

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Knowledge Extraction of Agricultural Data Using Artificial Immune System 12

Knowledge Extraction of Agricultural Data Using Artificial I...

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Lasisi, Ayodele Ghazali, Rozaida Herawan, Tutut Lasisi, Fola Deris, Mustafa Mat Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Obafemi Awolowo Univ Dept Agr Engn Ife Nigeria

ISBN: (纸本)9781467376822

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (clonalG) and artificial immune recognition system (AIRS) form the bedrock of this paper. A fuzzy-rough feature selection (FRFS) method is coupled with clonalG and AIRS for improved detection and computational efficiency. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the clonalG and AIRS produced significant results. Their respective FRFS upgrades namely;FRFS - clonalG and FRFS - AIRS are able to generate highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

关键词： data mining clonal selection algorithm artificial immune recognition system fuzzy-rough set

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An Adaptive Multi-objective clonal selection algorithm

An Adaptive Multi-objective Clonal Selection Algorithm

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2010 International Symposium on Computer,Communication, Control and Automation

作者： Lu Hong School of Electronic Engineering Huaihai Institute of Technology Lianyungang , China Zhicheng Ji School of Communications and Control Engineering Jiangnan University Wuxi , China

It is difficult for traditional search methods to solve multi-objective optimization problems. Based on the idea of clonal selection principle, we present an adaptive multi-objective clonal selection algorithm (AMCSA) for function optimization problems and analyze its powerful performance from the immune system point of view. The main feature of the algorithm is the global search performance and the solution sets produced are highly competitive in terms of convergence, diversity and distribution. The comparative simulation results show that the proposed algorithm not only can obtain a set of solutions including the global optimum and multiple local optima, but also has much less computational cost than other algorithms.

关键词： clonal selection algorithm multi-objective optimization Chaos

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Chaotic clonal selection algorithm for Function Optimization

Chaotic Clonal Selection Algorithm for Function Optimization

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2010 International Conference on E-Health Networking,Digital Ecosystems and Techonlogies(2010电子健康网络、数字生态系统和技术国际会议 EDT 2010)

作者： Hongwei Dai Yu Yang Cunhua Li School of Computer and Engineering Huaihai Institute of Technology Lianyungang Jiangsu 222005 China

ISBN: (纸本)9781424455140;9781424455157

The chaotic initialization and chaotic search are introduced into clonal selection algorithm (CSA) to overcome random antibody initialization and premature convergence problems in traditional CSA. Taking full advantages of the ergodic and stochastic properties of chaotic variables, antibodies with different affinity perform chaotic search to exploit local solution space. Experimental results on test functions demonstrate that the chaotic CSA outperforms the classical clonal selection algorithm.

关键词： clonal selection algorithm Chaotic initialization Chaotic search Optimization clonal selection selection algorithm initialization Solution space

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Chaotic clonal selection algorithm for Function Optimization

Chaotic Clonal Selection Algorithm for Function Optimization

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2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies

作者： Hongwei Dai, Yu Yang, Cunhua Li School of Computer and Engineering Huaihai Institute of Technology Lianyungang, Jiangsu, 222005 China

关键词： clonal selection algorithm chaotic initialization, chaotic search optimization

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