Computing minimal hitting set based on immune genetic algorithm

Abstract

The primary bottleneck of many model-based diagnosis approaches is computing minimal hitting sets (MHSs). Most existing algorithms for computing MHSs are deterministic. Such algorithms are sound and complete but with the complexity of exponential, since the problem of computing MHSs is NP-hard. To reduce the complexity, especially for large systems, we present a novel approach for multiple fault diagnosis, based on an immune genetic algorithm (IGA). This approach maps a hitting set problem onto an integer programming problem, followed by obtaining the lower bound on the size of the solution. The use of the bound in the immune operator guides the MHS search. An elite-set strategy and an appropriate termination criterion are proposed to improve the efficiency of the approach. Experimental results show that our approach solves MHSs faster than the traditional deterministic algorithm in large-scale problems. Compared with the incomplete algorithm, the speed and accuracy of our approach are also significantly improved.