Trend mining for system reliability employing a novel heuristic-based Kriging profiling method

Abstract

This paper presents a novel integration of heuristic-based regressor for the prediction of system reliability. This is implemented by integrating single layer perceptron (SLP) into Kriging model on the basis of an enhanced Particle Swarm Optimization. The proposed method is labeled here as heuristic SLP-based Kriging, or in short HSK. The backbone of HSK is a Competitive Niching-inspired PSO (CNPSO) that serves as the heuristic for identifying the core parameters of the SLP-based Kriging. CNPSO is composed of an opposition-based competitive initialization and a niching-inspired search scheme. For practicality and validation purposes, realistic datasets in the literature of system reliability are considered in the present study. The experimental results obtained demonstrated that HSK outperformed state-of-the-art methods proposed in the literature for addressing the same issue.