Regularity Model-Driven Large-Scale Multi-Objective Evolutionary Algorithm Based on Dual-Information Offspring Reproduction Strategy

The Pareto set (PS) of a continuous multi-objective optimization problem exhibit a distribution along a low-dimensional manifold structure. This regularity property significantly contributes to generating high-quality offspring in large-scale multi-objective evolutionary algorithms (LSMOEAs). However, conventional regularity model-based algorithms face several challenges when dealing with large-scale multi-objective optimization problems (LSMOPs), including high computational costs for modeling, difficulty in capturing the true PS structure, and neglecting individual directional information. To address these challenges, we propose a dual-information offspring reproduction strategy that considers both the distribution information of the population and the directional information of the outstanding individuals. Specifically, this strategy comprises a sampling approach based on an augmented regularity model specifically designed for LSMOPs. Leveraging this model, we explore and exploit the decision space to sample a promising set of solutions. Additionally, the strategy also involves a search method based on competitive learning among individuals. By assigning a positive evolutionary direction to losing solutions, we update the losing solutions to generate high-quality offspring. We continuously refine the proposed regularity model to approximate the true PS more closely. In extensive experiments on large-scale multi-objective benchmark functions, we compare our algorithm with eight state-of-the-art algorithms. The results demonstrate that our approach excels in handling LSMOPs.