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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Dai, Hongwei Yang, Yu Li, Cunhua Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Hybridization is confirmed as an effective way of combining the best properties of different algorithms and achieving better performances. A framework of hybrid crossover is constructed and combined with clonal selection algorithm (CSA). The new crossover solutions are generated by the mutual influence of both high affinity and low affinity solutions. Simulation results based on the traveling salesman problems demonstrate the effectiveness of the hybridization.

关键词： Hybrid crossover clonal selection algorithm Traveling salesman problem Simulation

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An Efficient clonal selection algorithm to Solve DynamicWeapon-target Assignment Game Model in UAV Cooperative Aerial Combat 35

An Efficient Clonal Selection Algorithm to Solve DynamicWeap...

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第35届中国控制会议

作者： WANG Yu ZHANG Weiguo LI Yong School of Automation Shenyang Aerospace University School of Automation Northwestern Polytechnical University School of Electrical Engineering Shenyang University of Technology

ISBN: (纸本)9781509009107

The environment of UAV cooperative aerial combat is complex and changeable,with strong ***,high accuracy and real-time are required.A multi-combat step UAV dynamic weapon-target assignment(DWTA) game model is established with the survival probability and weapons consumption factors for warring *** the solving method of bimatrix game Nash equilibrium point is applied to the model.A solving method based on clonal selection algorithm is *** algorithm considers both the diversity of population and the convergence *** results show the Nash equilibrium solution obtained by the algorithm is more accurate,which ensures the real-time and efficiency.

关键词： UAV cooperative aerial combat DWTA game model Nash equilibrium clonal selection algorithm

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Gray-Coded clonal selection algorithm for Optimization Problem 17th

Gray-Coded Clonal Selection Algorithm for Optimization Probl...

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Dai, Hongwei Yang, Yu Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China

ISBN: (纸本)9783319462578;9783319462561

clonal selection algorithm (CSA), inspired by the clonal selection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated immune operators, mutation, proliferation, selection, for example, are made to act on it. However, the binary representation often suffers from the so-called Hamming Cliff problem. In order to overcome this problem, a Gray-coded CSA is presented and used to solve optimization problems. The algorithm is applied to numerous bench-mark problems of numerical optimization problems and the computational results show effectiveness of the proposed algorithm.

关键词： clonal selection algorithm Gray code Optimization problem Simulation

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Application of clonal selection algorithm in Construction Site Utilization Planning Optimization

Application of Clonal Selection Algorithm in Construction Si...

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International Conference on Sustainable Design, Engineering and Construction (ICSDEC)

作者： Wang, Xi Deshpande, Abhijeet S. Dadi, Gabriel B. Salman, Baris Univ Kentucky Dept Civil Engn 382 Raymond Bldg Lexington KY 40506 USA Univ Cincinnati Dept Civil & Architectural Engn & Construct Manag 795 Rhodes Hall Cincinnati OH 45220 USA Syracuse Univ Dept Civil & Environm Engn 151H Link Hall Syracuse NY USA

Various algorithms inspired by evolutionary and physical processes have been extensively applied in solving complex construction engineering optimization problems. In this paper, Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, is introduced in terms of its basic mechanisms and its applications in construction engineering. Specifically, clonal selection algorithm (CSA), one of main algorithms that form AIS, is based on clonal selection process of the immune system which includes the selection, hypermutation, and receptor editing processes. We discuss the CSA in detail and present its application in the classic construction optimization problem, construction site utilization planning (CSUP), which is the decision making process for identifying the most optimal layout of temporary facilities designed to support the construction process. When applied to a test case published in research literature, we found that CSA shows a robust capacity to search the solution space effectively and efficiently. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Optimization Artificial Immune Systems clonal selection algorithm clonalG

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Improved clonal selection algorithm Optimizing Neural Network for Solving Terminal Anti-missile Collaborative Intercepting Assistant Decision-Making Model 1

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Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Xiao, Jin-ke Li, Wei-min Xiao, Xin-rong Lv, Cheng-zhong Air Force Engn Univ Xian 710051 Peoples R China South China Univ Technol Guangzhou 510640 Guangdong Peoples R China

ISBN: (数字)9789811026669

ISBN: (纸本)9789811026669;9789811026652

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is one key problem to enhance operational efficiency. Assistant decision-making model has been constructed after analysis on collaborative intercepting principle;then Improved clonal selection algorithm Optimizing Neural Network (IclonalG-NN) is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity, introducing modified combination operator to make use of information before crossover and mutation, introducing population update operator into traditional clonalG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of assistant decision-making model solved by IclonalG-NN.

关键词： Terminal anti-missile system Collaborative intercepting Assistant decision-making clonal selection algorithm Neural network

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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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Application of clonal selection algorithm in Construction Site Utilization Planning Optimization

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Procedia Engineering 2016年 145卷 267-273页

作者： Xi Wang Abhijeet S. Deshpande Gabriel B. Dadi Baris Salman Department of Civil Engineering 382 Raymond Building University of Kentucky Lexington KY 40506-0281 US Department of Civil & Architectural Engineering and Construction Management 795 Rhodes Hall University of Cincinnati Cincinnati OH US Department of Civil and Environmental Engineering 151H Link Hall Syracuse University Syracuse NY US

关键词： Optimization Artificial Immune Systems clonal selection algorithm clonalG

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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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A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem

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MEMETIC COMPUTING 2015年第3期7卷 181-201页

作者： Marinaki, Magdalene Marinakis, Yannis Tech Univ Crete Sch Prod Engn & Management Khania 73100 Greece

Nature inspired methods are approaches that are used in various fields and for the solution of a number of problems. This study uses a hybridized version of the clonal selection algorithm, the clonal selection algorithm-iterated local search-variable neighborhood search (CSA-ILS-VNS), for the solution of the feature selection problem (FSP). The clonal selection algorithm is inspired by the clonal selection and affinity maturation process of B cells of the natural immune system once the immune system has detected a pathogen. The proposed clonal selection algorithm is combined with a number of nearest neighbour based classifiers and it is tested using various benchmark data sets from the UCI machine learning repository. The algorithm is compared with variants of the clonal selection algorithm [the classic clonal selection algorithm (CSA), the clonal selection algorithm-iterated local search (CSA-ILS) and the clonal selection algorithm-variable neighborhood search (CSA-VNS)], a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm.

关键词： Artificial immune systems clonal selection algorithm Variable neighborhood search Feature selection problem Iterated local search

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A Novel clonal selection algorithm for Community Detection in Complex Networks

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COMPUTATIONAL INTELLIGENCE 2015年第3期31卷 442-464页

作者： Cai, Qing Gong, Maoguo Ma, Lijia Jiao, Licheng Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Recent years have seen the arising recognition of community detection in complex networks. Artificial immune systems, owing to their inherent properties, have been thoroughly studied and well applied to practical use. In this article, one of the well-known artificial immune system models, named clonal selection algorithm, is introduced to reveal community structures in complex networks. By introducing a novel antibody population initialization mechanism and a novel hypermutation strategy, the proposed approach could be applied to moderate-scale network. Besides, by optimizing an objective function called modularity density, the proposed algorithm is also capable of detecting community structure at multiple resolution levels. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

关键词： community detection artificial immune system clonal selection algorithm complex network modularity density

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