A Multiobjective Crystal Optimization-based association rule mining enhanced with TOPSIS for predictive maintenance analysis

Abstract

To obtain computer procedures that intelligently guide the search process by efficiently exploring the search space corresponds to an optimization problem that is solved using meta-heuristics. However, there are many optimization problems whose objective is to extract a single efficient solution that reflects the quality of the system performance. Hence, we also encounter multi-objective problems. This paper studies a multi-objective optimization problem that addresses the complexity of problems with synchronous indices. In this paper, association rule mining (ARM) is treated as an optimization problem. In recent literature, most of the proposed methods of ARM generate a large number of redundant and irrelevant rules. Therefore, we propose an improved multi-objective crystal structure algorithm using the TOPSIS approach. The experimental study is performed in two steps. First, we use a classical benchmark suite, namely WFG, to analyze the effectiveness of the proposed approach in generating solutions close to the Pareto fronts. In a second step, to confirm the performance of our algorithm, we apply it on the database of the Bosch Production line performance, and we conduct a comparative evaluation towards recent algorithms. The obtained results demonstrate the efficiency of the proposed algorithm in terms of number of rules, average support, average confidence, average conviction, average certain factor.