View article

There exist many optimal or heuristic priority rules for machine scheduling problems, which can easily be integrated into single-objective evolutionary algorithms via mutation operators. However, in the multi-objective case, simultaneously applying different priorities for different objectives may cause severe disruptions in the genome and may lead to inferior solutions. In this paper, we combine an existing mutation operator concept with new insights from detailed observation of the structure of solutions for multi-objective machine scheduling problems. This allows the comprehensive integration of priority rules to produce better Pareto-front approximations. We evaluate the extended operator concept compared to standard swap mutation and the stand-alone components of our hybrid scheme, which performs best in all evaluated cases.