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immune algorithm for Selecting Optimum Services in Web Services Composition

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 221-225页

作者： GAO Yan NA Jun ZHANG Bin YANG Lei GONG Qiang DAI Yu College of Information Science and Engineering Northeastern University Shenyang 110004 LiaoningChina

For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an immune algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.

关键词： Web services composition optimum selection immune algorithm

Learning Bayesian network structure with immune algorithm

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Journal of Systems Engineering and Electronics 2015年第2期26卷 282-291页

作者： Zhiqiang Cai Shubin Si Shudong Sun Hongyan Dui Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology School of Mechatronics Northwestern Polytechnical University

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

关键词： structure learning Bayesian network immune algorithm local optimal structure vaccination

Self-adaptive learning based immune algorithm

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Journal of Central South University 2012年第4期19卷 1021-1031页

作者：许斌庄毅薛羽王洲 Department of Computer Engineering&Science Nanjing University of Aeronautics&Astronautics Science and Technology on Electron-optic Control Laboratory

A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.

关键词： immune algorithm multi-modal optimization evolutionary computation immtme secondary response self-adaptivelearning

HIAWSC: An immune algorithm Based Heuristic Web Service Composition Framework

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Chinese Journal of Electronics 2014年第3期23卷 579-585页

作者： XU Jiuyun Stephan Reiff-Marganiec School of Computer and Communication Engineering China University of Petroleum Network and Switch Technology Key Laboratory Beijing University of Posts and Telecommunication Department of Computer Science University of Leicester

The introduction of of web services has led to web service composition being a focus of many researchers. Composing web services using workflows is seen as the most realistic method from an industrial viewpoint. Amongst other method, the use of natural computing methods has been proposed previously to automate web service composition. The need for a fast response when computing the most suitable sequence of services is addressed in this paper. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps: an immune selection operation, which is maintaining antibody population diversity and the clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence *** also provide a schema analysis for this method.

关键词： Web services composition immune algorithm Heuristic information Schema analysis

Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaos immune algorithm

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Journal of Central South University 2014年第2期21卷 593-599页

作者：王涛生左红艳 Research Center of Engineering Technology for Engineering Vehicle Chassis Manufacturing of Hunan Province Research Base of Multinational Investment and Operations in Hunan Province School of Resource and Safety Engineering Central South University

In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune *** simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and *** application results show that the relative errors of the soft measurement model are less than 3.34%.

关键词： chaos immune algorithm fuzzy support vector machine

Application of immune algorithm to Evaluation of Soil Resource Quality

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Pedosphere 2005年第5期15卷 660-668页

作者： YANG Hai-Dong HU Yue-Ming DENG Fei-Qi CHEN Fei-Xiang WANG Fei College of Automation South China University of Technology Guangzhou 510640 （China） College of Information South China Agricultural University Guangzhou 510642 （China） Department of Statistics Renmin University of China Beijing 100876 （China）

Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process andanalyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analysis method wasused in combining evaluation factors of Guangdong soil resource quality to determine the evaluation units. Because ofits favorable convergent speed and its ability to search solutions, the immune algorithm was applied to the soil resourcequality evaluation model. At the same time, the evaluation results of this newly proposed method were compared to twoother methods: sum of index and fuzzy synthetic. The results indicated that the immune algorithm reflected the actualcondition of soil resource quality more exactly.

关键词： evaluation GIS immune algorithm soil resources

Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect

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Journal of Modern Power Systems and Clean Energy 2017年第5期5卷 820-829页

作者： Baichao CHEN Liangliang WEI Yuanzhe ZHU Yongheng ZHONG Jiaxin YUAN Yang LEI School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the *** this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.

关键词： FCL optimal allocation Fault current mitigation effect Modified inversion operator Pareto optimal solutions immune algorithm

Optimizing neural network forecast by immune algorithm

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Journal of Central South University of Technology 2006年第5期13卷 573-576页

作者：杨淑霞李翔李宁杨尚东 School of Business Administration North China Electric Power University

Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.

关键词： neural network forecast immune algorithm optimization

immune algorithm for Solving the Optimization Problems of Computer Communication Networks

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The Journal of China Universities of Posts and Telecommunications 2000年第4期7卷 18-24页

作者： GAO Jie 1, WU Xin yu 2 (1. Department of Applied Mathematics and Physics, Nanjing University of Posts and Telecommunications, Nanjing 210003, P. R. China 2. Department of Electronical Engineering, Nanjing University of Posts and Telecommunications, Department of Applied Mathematics and Physics Nanjing University of Posts and Telecommunications Nanjing 210003 P. R. China Department of Electronical Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 P. R. China

The basic problem in optimizing communication networks is to assign a proper circuit for each origin destination pair in networks so as to minimize the average network delay, and the network optimal route selection model is a multi constrained 0 1 nonlinear programming problem. In this paper, a new stochastic optimization algorithm, immune algorithm, is applied to solve the optimization problem in communication networks. And the backbone network vBNS is chosen to illustrate the technique of evaluating delay in a virtual network. At last, IA is compared with the optimization method in communication networks based on Genetic algorithm, and the result shows that IA is better than GA in global optimum finding.

关键词： immune algorithm computer communication networks network delay optimal route select