A Large-Scale Expensive Multi-Objective Optimization Algorithm Assisted By Multiple Subproblems Collaboration

Over the past few years, a variety of surrogate-assisted evolutionary algorithms have emerged, aiming to tackle expensive multi-objective optimization problems. On the other hand, as the problem's dimensionality rises, the sample size needed for training surrogate models also increases significantly, making this approach impractical for large-scale scenarios. To overcome this challenge, we propose a large-scale expensive multi-objective optimization algorithm leverages collaboration among multiple subproblems. Specifically, we employ random feature selection with multiple times to partition the original decision space into multiple subproblems. For each objective, a surrogate model is constructed based on the historical data of each subproblem. After searching for the surrogate models of each subproblem, the population of original problem is updated. Additionally, to accelerate optimization, we select m individuals with the best performance for each objective, as well as the one nearest to the ideal point, for true evaluations. To illustrate the effectiveness of our approach, comparative experiments have been carried out using the DTLZ and ZDT benchmark problems, which involve up to 200 decision variables. The experimental outcomes indicate that the proposed technique delivers strong performance in addressing large-scale, expensive multi-objective optimization tasks.