A generalized extremal optimization-inspired algorithm for predictive maintenance scheduling problems

A bit-encoded heuristic evolutionary optimization algorithm inspired by the Generalized Extremal Optimization method is presented. The proposed evolutionary approach aims at optimizing a predictive maintenance scheduling problem characterized by an analytically intractable objective function. A preliminary comparison with a standard genetic algorithm on a set of high-dimension cases of the considered maintenance problem shows better performance for the proposed approach.