A Learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution

Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.