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检索条件"主题词=Clonal selection algorithm"

共 233 条记录，以下是101-110 订阅

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Design of Optimizing Stack Filters by an Ant Colony-clonal selection algorithm

Design of Optimizing Stack Filters by an Ant Colony-Clonal S...

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Chinese Conference on Pattern Recognition/1st CJK Joint Workshop on Pattern Recognition

作者： Tian, Ye Zhao, Chunhui Harbin Normal Univ Coll Phys & Elect Engn Harbin 150025 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781424441990

Stack filters are a class of nonlinear digital filters that satisfy a weak superposition property known as the threshold decomposition and stacking property. They have good performances of suppressing noises and preserving details. Their design is formulated as a course of optimizing PBF. This paper presents an ant colony-clonal selection algorithm for stack filters' optimizing. Under the MMAE criterion, the capability of the stack filters optimized by this method has been proven through simulation. And the results prove its advancing compared with the stack filters optimized by genetic algorithm, particle swarm algorithm and clonal selection algorithm.

关键词： Stack filters clonal selection algorithm Ant colony algorithm MMAE criterion Image processing

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Function Optimization Based on Improved clonal selection algorithm

Function Optimization Based on Improved Clonal Selection Alg...

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2012年第14届中国系统仿真技术及其应用学术年会

作者： Xi CHEN Yuxuan DING Jianjun HE School of Information and Communication Engineering Hunan Institute of Science and Technology Yuey College of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuh School of Information Science and Engineering Central South University Changsha Hunan China

An improved clonal selection algorithm is proposed for the two shortcomings of the traditional algorithm. Improved contents are as follows: An adaptive improved mutation operator is designed. And we put forward an improved selection operator which takes into account two factors of affinity and concentration. The simulation results show the improved algorithm is superior to the traditional clonal selection algorithm in convergence speed and optimizing result.

关键词： clonal selection algorithm affinity weight factor convergence speed function optimization

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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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Solving DNA motif discovery problem using improved clonal selection algorithm with tournament selection operator: DNA motif discovery with clonal selection algorithm 19

Solving DNA motif discovery problem using improved Clonal se...

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Proceedings of the 1st International Conference on Advanced Information Science and System

作者： Ezgi Deniz Ülker European University of Lefke Turkey

ISBN: (纸本)9781450372916

Identifying the regulatory elements in deoxyribonucleic acid (DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem (MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved clonal selection algorithm with Tournament selection operator (ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.

关键词： clonal selection algorithm tournament selection biological optimization motif discovery

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Solving DNA Motif Discovery Problem using Improved clonal selection algorithm with Tournament selection Operator DNA Motif Discovery with clonal selection algorithm

Solving DNA Motif Discovery Problem using Improved Clonal Se...

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作者： Ezgi Deniz ülker Department of Computer Engineering European University of Lefke

Identifying the regulatory elements in deoxyribonucleic acid(DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem(MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved clonal selection algorithm with Tournament selection operator(ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.

关键词： Biological optimization clonal selection algorithm Motif discovery Tournament selection

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Hybrid clonal selection algorithm with PSO for Valve- Point Economic Load Dispatch

Hybrid CLONAL Selection Algorithm with PSO for Valve- Point ...

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IEEE Mediterranean Electrotechnical Conference

作者： Sogol Babaei Nejad Seyyed Hamid Elyas Ali Khamseh Iman Naziri Moghaddam Mehdi Karrari Department of Electrical Engineering Semnan University Department of Electrical Engineering Amir Kabir University of Technology

ISBN: (纸本)9781467307826

Economic load Dispatch (ED) is one of the most important problems in power system operation. It is a nonlinear non-convex problem which stochastic search algorithms seem to be appropriate solutions. This study tries to propose a new method which is derived from the combination of two different algorithms, clonal as the basic algorithm and PSO. The proposed method has been tested on two different systems containing thirteen and forty generators and obtained results have been compared with the results of other stochastic search algorithms. The fascinating results obtained from the comparison ensure the efficiency of the new proposed method.

关键词： Economic Load Dispatch clonal selection algorithm Particle Swarm Optimization

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Function Optimization Based on Improved clonal selection algorithm

Function Optimization Based on Improved Clonal Selection Alg...

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第14届中国系统仿真技术及其应用学术年会

作者： Xi CHEN 1,Yuxuan DING 2,Jianjun HE 3 1 School of Information and Communication Engineering,Hunan Institute of Science and Technology,Yueyang,Hunan,China 2 College of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan,China 3 School of Information Science and Engineering,Central South University,Changsha Hunan,China

An improved clonal selection algorithm is proposed for the two shortcomings of the traditional *** contents are as follows:An adaptive improved mutation operator is *** we put forward an improved selection operator which takes into account two factors of affinity and *** simulation results show the improved algorithm is superior to the traditional clonal selection algorithm in convergence speed and optimizing result.

关键词： clonal selection algorithm affinity weight factor convergence speed function optimization

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A clonal selection algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

A Clonal Selection Algorithm with Levenshtein Distance based...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Takumi Ichimura Shin Kamada Faculty of Management and Information Systems Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan Graduate School of Comprehensive Scientific Research Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan

ISBN: (纸本)9781479906505

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and clonal selection algorithm with Immunological Memory Cells in this paper.

关键词： Levenshtein Distance clonal selection algorithm Image Analysis Immunological Memory Cells Growing Hierarchical SOM Interactive GHSOM Smartphone based Participatory Sensing System Tourist Informatics Knowledge Discovery Levenshtein distance data mining image analysis images selection algorithm Tourist information services clonal selection Interactive Device Sensor CFC1 gene DATA ACQUISITION SYSTEMS

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An Improved clonal selection algorithm for Job Shop Scheduling

An Improved Clonal Selection Algorithm for Job Shop Scheduli...

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2009 International Symposium on Intelligent Ubiquitous Computing and Education(2009国际智能普适计算与教育研讨会 IUCE 2009)

作者： Hong Lu Jing Yang Department of Electronic Engineering Huaihai Institute of Technology Lianyungang China College of Electric Engineering Guizhou University Guiyang China

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.

关键词： artificial immune systems clonal selection algorithm job shop scheduling problem

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