An Adaptive Dual-Archive SPEA2 For Solving Multi-Objective Flexible Job Shop Scheduling Problems

For high-dimensional multi-objective flexible job shop scheduling problems, balancing solution diversity and convergence is crucial. However, existing studies often focus on either diversity or convergence alone, overlooking the potential of optimizing both synergistically. To address this, we introduced a multi-objective flexible job shop scheduling model with makespan, total machine energy consumption, and total machine load as the optimization objectives. We proposed an Adaptive Dual-Archive Strength Pareto Evolutionary Algorithm (ADASPEA2) to improve performance on high-dimensional multi-objective optimization problems. This algorithm proposed primary and auxiliary archives to retain elite solutions and maintain solution diversity, respectively, while dynamically adjusting an adaptive fitness threshold to balance search and convergence. Experimental results confirm the feasibility of this algorithm for multi-objective flexible job shop scheduling optimization, and benchmark tests demonstrate significant advantages in both convergence and diversity.