A Two-Stage Adaptive Large-Scale Evolutionary Algorithm via PCA-Guided Perturbation

Large-scale multi-objective optimization problems (LSMOPs) face computational challenges due to high-dimensional search spaces and variable correlations. Existing algorithms often struggle to balance convergence, diversity, and efficiency in high-dimensional scenarios. We propose ATSPCA, a two-stage adaptive algorithm employing PCA-guided perturbation. The first stage uses PCA-based dynamic variable screening to identify key decision variable groups, applying adaptive Gaussian perturbation to guide optimization. The second stage enhances convergence and diversity through an improved competitive swarm optimizer (CSO) and reference pointbased environmental selection. An adaptive transition mechanism bridges the two stages. Tested on LSMOP and WFG benchmarks against state-of-the-art large-scale multi-objective evolutionary algorithms(LSMOEAs), ATSPCA demonstrates superior performance.