An Algorithm for Large-Scale Multi-Objective Optimization Problems Digital Twin-Multi-Layer Cooperative Evolutionary Algorithm

Large-scale multi-objective optimization problems involve high-dimensional decision variables, vast search spaces, and conflicting objectives, which often lead to slow convergence, premature convergence, and poorly distributed solutions when conventional evolutionary algorithms operate under limited evaluation budgets. To address these challenges, this study proposes DT-MLCEA (Digital Twin-Multi-Layer Cooperative Evolutionary Algorithm), a digital twin-driven, multi-layer cooperative evolutionary algorithm. The method uses evolutionary search as its core and introduces a search-state-oriented digital twin feedback mechanism that jointly models convergence trends, diversity, distribution sparsity, stagnation, and state confidence. A multi-source adaptive control vector (MACV) is then generated to adjust key search parameters dynamically. The digital twin layer further integrates Mini-RL, Mini-AE, and Mini-Meta to refine its control recommendations and balance global exploration with local exploitation. DT-MLCEA is evaluated against LMEA, QLMGO, AMSLMOEA, and MOEA/D-RDG on LSMOP1-LSMOP9 with 100, 500, 1000, and 5000 decision variables. Across 36 problem-dimension combinations, DT-MLCEA significantly outperforms the competing algorithms in terms of HV and IGD in 28 and 24 combinations, respectively. Extended experiments on LSMOP1-LSMOP3 indicate stable performance with up to 50,000 variables. Ablation, noisy-evaluation, and parameter-sensitivity analyses further demonstrate the effectiveness of the collaborative modules.