The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Song Wang - One of the best experts on this subject based on the ideXlab platform.
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a novel chaos Danger Model immune algorithm
Communications in Nonlinear Science and Numerical Simulation, 2013Co-Authors: Song Wang, Li Zhang, Ying LiangAbstract:Abstract Making use of ergodicity and randomness of chaos, a novel chaos Danger Model immune algorithm (CDMIA) is presented by combining the benefits of chaos and Danger Model immune algorithm (DMIA). To maintain the diversity of antibodies and ensure the performances of the algorithm, two chaotic operators are proposed. Chaotic disturbance is used for updating the Danger antibody to exploit local solution space, and the chaotic regeneration is referred to the safe antibody for exploring the entire solution space. In addition, the performances of the algorithm are examined based upon several benchmark problems. The experimental results indicate that the diversity of the population is improved noticeably, and the CDMIA exhibits a higher efficiency than the Danger Model immune algorithm and other optimization algorithms.
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structural design of the Danger Model immune algorithm
Information Sciences, 2012Co-Authors: Song Wang, Caixia ZhangAbstract:The traditional immune algorithm (IA) is based on a self-nonself biological immunity mechanism. Recently, a novel immune theory called the Danger Model theory has provided more suitable biological information for data handling compared with the self-nonself mechanism. According to the Danger Model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model Immune Algorithm (DMIA) that differs from the traditional IA in terms of the self-nonself biological immunity mechanism. We define a Danger area and a Danger signal in DMIA. We use the selection, mutation, and specific Danger operators to update the population. The algorithm can achieve complex problem optimization. Simulation studies demonstrate that DMIA exhibits a higher efficiency than traditional genetic algorithms and other algorithms when considering a number of complicated functions.
Chuang Zhang - One of the best experts on this subject based on the ideXlab platform.
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research on Danger Model theory based artificial immune algorithm
Computational Intelligence, 2009Co-Authors: Xianyao Meng, Ning Wang, Chuang ZhangAbstract:Inspired from the experience of genetic and clone algorithm, propose an algorithm of Danger Model Immune Algorithm(DMIA) according to the Danger Model theory. It has a good performance in function optimization. From the simulation result, we can see that DMIA is valid, and also with higher efficiency than genetic algorithm. Keywords-Danger Model Immune Algorithm; Danger signal; Danger area; optimization
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adaptive Danger area based Danger Model immune algorithm
International Conference on Intelligent Computing, 2009Co-Authors: Xianyao Meng, Ning Wang, Chuang ZhangAbstract:Danger Model Immune Algorithm(DMIA) is an algorithm based on the Danger theory of biological immune system. In the basic algorithm, the Danger area is fixed through the initial setting. It is an important parameter which will affect the capability of algorithm. In this paper, propose an adaptive Danger area DMIA. The radius of Danger area is decrease gradually according to the iteration steps. The simulation results indicate that the adaptive Danger area DMIA is valid and has better optimization capability.
Xianyao Meng - One of the best experts on this subject based on the ideXlab platform.
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research on Danger Model theory based artificial immune algorithm
Computational Intelligence, 2009Co-Authors: Xianyao Meng, Ning Wang, Chuang ZhangAbstract:Inspired from the experience of genetic and clone algorithm, propose an algorithm of Danger Model Immune Algorithm(DMIA) according to the Danger Model theory. It has a good performance in function optimization. From the simulation result, we can see that DMIA is valid, and also with higher efficiency than genetic algorithm. Keywords-Danger Model Immune Algorithm; Danger signal; Danger area; optimization
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adaptive Danger area based Danger Model immune algorithm
International Conference on Intelligent Computing, 2009Co-Authors: Xianyao Meng, Ning Wang, Chuang ZhangAbstract:Danger Model Immune Algorithm(DMIA) is an algorithm based on the Danger theory of biological immune system. In the basic algorithm, the Danger area is fixed through the initial setting. It is an important parameter which will affect the capability of algorithm. In this paper, propose an adaptive Danger area DMIA. The radius of Danger area is decrease gradually according to the iteration steps. The simulation results indicate that the adaptive Danger area DMIA is valid and has better optimization capability.
Caixia Zhang - One of the best experts on this subject based on the ideXlab platform.
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structural design of the Danger Model immune algorithm
Information Sciences, 2012Co-Authors: Song Wang, Caixia ZhangAbstract:The traditional immune algorithm (IA) is based on a self-nonself biological immunity mechanism. Recently, a novel immune theory called the Danger Model theory has provided more suitable biological information for data handling compared with the self-nonself mechanism. According to the Danger Model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model Immune Algorithm (DMIA) that differs from the traditional IA in terms of the self-nonself biological immunity mechanism. We define a Danger area and a Danger signal in DMIA. We use the selection, mutation, and specific Danger operators to update the population. The algorithm can achieve complex problem optimization. Simulation studies demonstrate that DMIA exhibits a higher efficiency than traditional genetic algorithms and other algorithms when considering a number of complicated functions.
Ying Liang - One of the best experts on this subject based on the ideXlab platform.
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a novel chaos Danger Model immune algorithm
Communications in Nonlinear Science and Numerical Simulation, 2013Co-Authors: Song Wang, Li Zhang, Ying LiangAbstract:Abstract Making use of ergodicity and randomness of chaos, a novel chaos Danger Model immune algorithm (CDMIA) is presented by combining the benefits of chaos and Danger Model immune algorithm (DMIA). To maintain the diversity of antibodies and ensure the performances of the algorithm, two chaotic operators are proposed. Chaotic disturbance is used for updating the Danger antibody to exploit local solution space, and the chaotic regeneration is referred to the safe antibody for exploring the entire solution space. In addition, the performances of the algorithm are examined based upon several benchmark problems. The experimental results indicate that the diversity of the population is improved noticeably, and the CDMIA exhibits a higher efficiency than the Danger Model immune algorithm and other optimization algorithms.
