Skip to main navigation menu Skip to main content Skip to site footer

Articles

Vol. 6 No. 3 (2019)

Fault Detection using Negative Selection and Genetic Algorithms

  • ABID Anam
  • HAQ Zia Ul
  • KHAN Muhammad Tahir
Submitted
February 5, 2024
Published
2024-02-05

Abstract

In this paper, negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors. The main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified detectors. Conventionally, researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone; however, detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself space. In approach proposed in the present paper, the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the detectors. In the proposed methodology, a novel diversity factor-based approach is presented to obtain diversified detector distribution in the nonself space. The concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral self-patterns. Furthermore, the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings.

Downloads

Download data is not yet available.

Similar Articles

1 2 3 4 5 6 7 8 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)