A Two-Stage Large-Scale Multiobjective Evolutionary Algorithm Based on Offset Direction Sampling and Dual-Layer Competition

Large-scale multiobjective optimization problems are characterized by high-dimensional decision spaces and complex search landscapes. These challenges create a dilemma for balancing convergence and diversity in the objective space under limited function evaluations. To address this issue, this paper proposes a two-stage large-scale multiobjective evolutionary algorithm based on offset direction sampling and dual-layer competition. In the rapid convergence stage, a set of high-quality solutions is used to construct search directions in the decision space, followed by sampling along these directions to assist faster convergence of the population. In the precise convergence stage, all individuals are divided into elite and non-elite layers based on non-dominated ranks. Through competition between different layers, two outstanding individuals are identified to further guide the population toward the Pareto-optimal front. Experimental evaluations on benchmark demonstrate significant competitive advantage of the proposed algorithm in addressing large-scale multiobjective optimization challenges involving up to 5000 decision variables.