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HD-NSA: A real-valued negative selection algorithm based on hierarchy division
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DOI：10.1016/j.asoc.2021.107726.png)
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Abstract
En 中文
The negative selection algorithm (NSA) is an important algorithm for generating immune detectors in artificial immune systems. However, the original NSA randomly generates candidate detectors that produce a large number of redundant detectors, and it is difficult to cover the entire antibody space. Moreover, the randomly generated candidate detectors have to be compared with all the self-sets; therefore, the inefficient generation of the detector seriously influences the application of NSA. To overcome these defects, a real-valued NSA based on hierarchy division (HD-NSA) is proposed. First, the feature space is divided into self and non-self subgrids, and the center point of the non-self subgrid is specified as the candidate detector, and the specified candidate detector is compared with the self-antigens located in adjacent subgrids rather than with all the self-sets. Theoretical analysis demonstrated that the HD-NSA can effectively reduce the time complexity of the NSA algorithm. Furthermore, experiments on the Abalone data set show that the detector training time of HD-NSA decreased by 97.9%, 71.2%, 56.9% and 90.1%, respectively, compared with the classical RNSA, V-Detector, GF-RNSA and BIORV-NSA, whereas the detector detection rate increased by 50%, 25.8%, 13.8% and 10.5%, respectively. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Artificial immune system
Negative selection algorithm
Hierarchical division
Specified candidate detector
Hole repair
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