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Adaptive Particle Swarm Optimization Based Artificial Immune Network Classification Algorithm

Ruochen Liu, Manchun Niu, Lina Tang, Licheng Jiao

Abstract


Artificial immune network algorithm (AIN) is a new computational intelligence method. The mutation operators in most of existed artificial immune network algorithms for classifier are random mutation, which leads to a just passable search ability and low classification accuracy. In order to overcome such problem and guide the B-cells to evolve in optimal direction, an adaptive Particle Swarm Optimization (PSO) is introduced into AIN as a new mutation operation and a new classification algorithm - Adaptive PSO based Artificial Immune Network Classification algorithm (APAINC) is proposed. The proposed algorithm has been extensively compared with Artificial Immune Network Classification algorithm based on random mutation (AINC) and Artificial Immune Network Classification Algorithm based on PSO (PSOAINC) over four UCI data sets with large size and two artificial texture images and three SAR images. The result of experiment indicates the superiority of APAINC over AINC and PSOAINC on classification accuracy.

Keywords


SAR image; PSO

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