BIORV-NSA: Bidirectional inhibition optimization r-variable negative selection algorithm and its application

A bidirectional inhibition optimization r-variable negative selection algorithm (BIORV-NSA) is proposed to generate less mature detectors and cover more "black holes".BIORV-NSA algorithm is composed of two sub algorithms that are self set edge inhibition strategy and detector self-inhibition strategy.When doing comparative experiments, BIORV-NSA is divided into two kinds of algorithms, which are a bidirectional weak inhibition optimization r-variable negative selection algorithm and a bidirectional strong inhibition optimization r-variable negative selection algorithm, respectively. The original negative selection algorithm (NSA) has the disadvantages that many "black holes" cannot be detected and excessive invalid detectors are generated. To overcome its defects, this paper improves the detection performance of NSA and presents a kind of bidirectional inhibition optimization r-variable negative selection algorithm (BIORV-NSA). The proposed algorithm includes self set edge inhibition strategy and detector self-inhibition strategy. Self set edge inhibition strategy defines a generalized radius for self individual area, making self individual radius dynamically be variable. To a certain extent, the critical antigens close to self individual area are recognized and more non-self space is covered. Detector self-inhibition strategy, aiming at mutual cross-coverage among mature detectors, eliminates those detectors that are recognized by other mature detectors and avoids the production of excessive invalid detectors. Experiments on artificially generating data set and two standard real-world data sets from UCI are made to verify the performance of BIORV-NSA, by comparison with NSA and R-NSA, the experimental results demonstrate that the proposed BIORV-NSA algorithm can cover more non-self space, greatly improve the detection rates and obtain better detection performance by using fewer mature detectors.