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Optimal Data Clustering by Using Artificial Immune Network with elitist learning

Optimal Data Clustering by Using Artificial Immune Network w...

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第26届中国控制与决策会议

作者： Li Zhonghua Fang Xianshu Zhou Jieying School of Information Science and Technology Sun Yat-sen University

ISBN: (纸本)9781479937097

Data clustering is one of the most popular techniques in data *** attributes of the objects within the same cluster should be similar to each other,while the attributes of the objects within different clusters should be *** of classical clustering algorithms suffer from such disadvantages as local optima,initial centroid selection and the lack of a way to automatically determine the optimal number of clusters,and thus fail to cluster datasets with complex *** paper proposes a novel clustering algorithm based on artificial immune network with elitist learning（Ocopt-aiNet-EL）.Some elaborated operators are implemented for automatic data clustering and the Manifold distance is used for the dissimilarity measure of antibodies.A number of benchmark cases including artificial datasets and real-world datasets are arranged to evaluate the performance of the proposed Ocopt-aiNet-EL algorithm and the other clustering algorithms,such as K-means,EAC and *** experimental results demonstrate that the proposed Ocopt-aiNet-EL is an effective and efficient clustering method.

关键词： Artificial immune network data clustering elitist learning entropy measure manifold distance

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An Improved Artificial Immune Network for Multimodal Function Optimization

An Improved Artificial Immune Network for Multimodal Functio...

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第26届中国控制与决策会议

作者： LI Zhonghua LI Jianming ZHOU Jieying School of Information Science and Technology Sun Yat-sen University

Multimodal optimization problems are typical those problems where both a global optimum and one or more local optima are included, which can be tackled with by evolutionary algorithms. This paper proposes an improved artificial immune network for multimodal function optimization. In the antibody population, antibodies are allocated to three spaces, i.e., the elitist space, the common space and the poor space. To be specific, the antibodies in the elitist space undergo self-learning mutation, those in the common space undergo elitist-learning mutation and those in the poor space undergo the random hypermutation. At each generation, the mutated antibodies with higher affinity are regrouped into a new elitist space while those with medium affinity are regrouped into a new common space. On the other hand, the old poor space is removed;instead, a group of randomly generated antibodies are grouped into a new poor space. Six benchmark functions in both 10 D and 30 D are used to evaluate the optimization performance of the proposed artificial immune network and the other existing artificial immune networks, such as opt-aiNet, IA-AIS and AINet-SL. The simulation results indicate that the proposed artificial immune network is an effective and relative efficient method in optimizing both the unimodal and multimodal functions.

关键词： Artificial immune network multimodal optimization elitist learning dynamic updating

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