检索结果-内蒙古大学图书馆

2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Du, Xianjun Hao, Xiaohong Yu, Ping Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9783037853849

A clonal selection algorithm (CSA) was used in this article to the wastewater biological treatment activated sludge process optimization with the lowest operational costs by limiting total substrate discharge mass. A simulation result shows that the method is efficiency, and it also provides a new approach for other optimal control problem.

关键词： clonal selection algorithm optimization activated sludge process wastewater biological treatment

来源：评论

学校读者我要写书评

暂无评论

Empirical Study of Population-Based Dynamic Constrained Multimodal Optimization algorithms

Empirical Study of Population-Based Dynamic Constrained Mult...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Lin, Xin Luo, Wenjian Qiao, Yingying Xu, Peilan Zhu, Tao Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Software Engn Comp & Commun Hefei 230027 Anhui Peoples R China Univ South China Sch Software Engn Hengyang 421001 Hunan Peoples R China

ISBN: (纸本)9781728124858

There are many dynamic optimization problems in real-world applications. Although many variants of evolutionary algorithms and swarm intelligence have been proposed to solve such problems, little work has been conducted to address the dynamic constrained multimodal optimization problems (DCM-MOPs). In DCMMOPs, there exist multiple optimal solutions corresponding to each environment that the algorithm is required to find. Sometimes, it is also necessary to identify the accepted local optima. Therefore, for each environment, the decision maker can select one from among multiple returned solutions according to his/her domain knowledge and/or preferences. The objective of this paper is to test the performance of various combinations of several population-based dynamic multimodal optimization algorithms and popular constraint handling techniques. First, the typical dynamic constrained optimization problems are slightly modified to be in the form of the dynamic constrained multimodal optimization problems. Second, four different population-based dynamic multimodal optimization algorithms, and five different constraint handling techniques, are pairwise tested in the experiments. Experimental results demonstrate that, among the candidates, DCMM-CSA-SR performs most successfully at all accuracy levels.

关键词： Dynamic optimization multimodal optimization constrained optimization evolutionary computation clonal selection algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

引用

PATTERN ANALYSIS AND APPLICATIONS 2018年第3期21卷 741-754页

作者： Lu, Xiao-Yong Chen, Mu-Sheng Wu, Jheng-Long Chang, Pei-Chan Chen, Meng-Hui Nanchang Univ Sch Software Engn Nanchang 330047 Jiangxi Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Software Sch Nanchang 330031 Jiangxi Peoples R China Acad Sinica Inst Informat Sci Taipei 115 Taiwan Yuan Ze Univ Informat Management & Innovat Ctr Big Data & Digi Taoyuan 32003 Taiwan

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.

关键词： Web spam detection Ensemble learning clonal selection algorithm Feature selection Decision trees

来源：评论

学校读者我要写书评

暂无评论

Infrared image segmentation using growing immune field and clone threshold

引用

INFRARED PHYSICS & TECHNOLOGY 2018年 88卷 184-193页

作者： Yu, Xiao Zhou, Zijie Gao, Qiang Li, Dahua Riha, Kamil Tianjin Univ Technol Sch Elect & Elect Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Brno Univ Technol Dept Telecommun Brno 61200 Czech Republic

Fast and accurate segmentation of infrared target is the basis of automatic target recognition, but there is a problem that it is easy to appear the significant differences of target areas in segmentation. In order to solve this problem, in this paper a new method based on growing immune field and clone threshold for segmentation of infrared targets is introduced. First, according to the global gray information, obtain the best threshold of the image using the clonal selection algorithm for global threshold segmentation. And the seed region is selected based on global threshold segmentation. Second, the source seeds are obtained by comparing the similarity threshold with seed region. Third, the growing immune field is adjusted automatically for region growing through the source seeds. Finally, the segmented image is obtained by immune region growing. The simulation results show that the target information gained by the proposed method is complete and exact. This resultgreatly facilitates the target recognition. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Infrared image clonal selection algorithm Region growing Growing immune field

来源：评论

学校读者我要写书评

暂无评论

Detecting RTL Trojans Using Artificial Immune Systems and High Level Behavior Classification

Detecting RTL Trojans Using Artificial Immune Systems and Hi...

引用

作者： Zareen, Farhath University of South Florida

学位级别：M.S.

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need fora golden reference model. We explore the use of Negative selection and clonal selection algorithms, which have historically been applied to malware detection on software binaries, to detect potentially unsafe or malicious behavior in hardware. We present a software tool which analyzes Trojan-inserted benchmarks, extracts their control and data-flow graphs (CDFGs), and uses this to train an AIS behavior model, against which new hardware descriptions may be tested.

关键词： clonal selection algorithm Control/Data-Flow Graphs Hardware Security Hardware Trojans Negative selection algorithm

来源：评论

学校读者我要写书评

暂无评论

Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system

引用

ALEXANDRIA ENGINEERING JOURNAL 2018年第1期57卷 235-245页

作者： Hatata, A. Y. Abd-Raboh, El-H. Sedhom, Bishoy E. Mansoura Univ Fac Engn Mansoura Egypt

Sandia frequency shift (SFS) is one of the active anti-islanding detection methods that depend on frequency drift to detect an islanding condition for inverter-based distributed generation. The non-detection zone (NDZ) of the SFS method depends to a great extent on its parameters. Improper adjusting of these parameters may result in failure of the method. This paper presents a proposed artificial immune system (AIS)-based technique to obtain optimal parameters of SFS anti-islanding detection method. The immune system is highly distributed, highly adaptive, and self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. The proposed method generates less total harmonic distortion (THD) than the conventional SFS, which results in faster island detection and better non-detection zone. The performance of the proposed method is derived analytically and simulated using Matlab/Simulink. Two case studies are used to verify the proposed method. The first case includes a photovoltaic (PV) connected to grid and the second includes a wind turbine connected to grid. The deduced optimized parameter setting helps to achieve the "non-islanding inverter" as well as least potential adverse impact on power quality. (C) 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.

关键词： Anti-islanding detection Sandia frequency shift (SFS) Non-detection zone (NDZ) Total harmonic distortion (THD) Artificial immune system (AIS) clonal selection algorithm

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Metaheuristic Approaches for Multidimensional Two-Way Number Partitioning Problem

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 7499-7520页

作者： Hacibeyoglu, Mehmet Alaykiran, Kemal Acilar, Ayse Merve Tongur, Vahit Ulker, Erkan Necmettin Erbakan Univ Dept Comp Engn Konya Turkey Necmettin Erbakan Univ Dept Ind Engn Konya Turkey Selcuk Univ Dept Comp Engn Konya Turkey

In this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimization algorithm (MBO) and clonal selection algorithm (CSA) are applied to multidimensional two-way number partitioning problem (MDTWNPP). MDTWNPP is a classical combinatorial NP-hard optimization problem where a set of vectors have more than one coordinate is partitioned into two subsets. The main objective function of MDTWNPP is to minimize the maximum absolute difference between the sums per coordinate of elements. In order to solve this problem, GA is applied with greedy crossover and mutation operators. SA is improved with dual local search mechanism. MBO is specialized as multiple flock migrating birds optimization algorithms. CSA is applied with problem specific hyper mutation process. Furthermore, all instances are solved using an integer linear programming model which was previously presented in the literature. In the experiments, four metaheuristic approaches and integer linear programming model are used to solve 126 datasets with different sizes and coordinates. As a brief result, the GA and SA approaches designed for this problem outperformed all other heuristics and the integer programming model. Both the performance of GA and SA approaches are in a competitive manner where GA and SA yielded the best solution for 56 and 65 out of 125 datasets, respectively.

关键词： Multidimensional two-way number partitioning problem Integer linear programming Genetic algorithms Migrating birds optimization algorithm clonal selection algorithm Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

引用

JOURNAL OF GLOBAL OPTIMIZATION 2018年第3期72卷 491-515页

作者： Patane, Andrea Santoro, Andrea Romano, Vittorio La Magna, Antonino Nicosia, Giuseppe Univ Oxford Dept Comp Sci Oxford England Queen Mary Univ London Sch Math Sci London England Univ Catania Dept Math & Comp Sci Catania Italy CNR Ist Microelettron & Microsistemi Catania Italy Univ Reading Dept Comp Sci Reading Berks England Univ Cambridge Syst Biol Ctr Cambridge England

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon's scattering probability in systems presenting textured interfaces;(b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures;(c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance;(d) the models are investigated by qualitative and quantitativesensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space;(e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost);(f) Local, Global and Glocal robustness of optimal solutions found by the optimization algorithms are statistically evaluated;(g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices.

关键词： Thin-film silicon solar cell Quantum efficiency Photonic Monte Carlo Multi-objective optimization OptIA clonal selection algorithm Morris method Sobol indexes Robustness analysis

来源：评论

学校读者我要写书评

暂无评论

A binary clonal flower pollination algorithm for feature selection

引用

PATTERN RECOGNITION LETTERS 2016年 77卷 21-27页

作者： Sayed, Safinaz AbdEl-Fattah Nabil, Emad Badr, Amr Cairo Univ Dept Comp Sci Cairo Egypt

Feature selection problem has been detected essentially in the last years. It is a step that is considered the prerequisite of the classification step. For the feature selection problem, the goal is to find out the most important subset of features that represent the original features in a certain domain. The selected features are used in optimization of a certain fitness function, so the feature selection problem can be seen as an optimization problem. This paper presents a new hybrid algorithm that combines clonal selection algorithm (CSA) with Flower Pollination algorithm (FPA) to compose Binary clonal Flower Pollination algorithm (BCFA) to solve the feature selection problem. The accuracy of the Optimum-Path Forest (OPF) classifier is used as an objective function. The experiments were implemented on three public datasets and demonstrated that the proposed hybrid algorithm achieved remarkable results in comparison with other well-known algorithms such as Binary Cuckoo Search algorithm (BCSA), Binary Bat algorithm (BBA), Binary Differential Evolution algorithm (BDEA) and Binary Flower Pollination algorithm (BFPA). (C) 2016 Elsevier B.V. All rights reserved.

关键词： Feature selection clonal selection algorithm Flower Pollination algorithm Optimum Path Forest

来源：评论

学校读者我要写书评

暂无评论

Function Optimization Based on Improved clonal selection algorithm

Function Optimization Based on Improved Clonal Selection Alg...

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：