Abstract: Large-scale multi-objective optimization problems are widely present in engineering fields.As the number of decision variables increases,obtaining high-quality offspring within limited resources becomes extremely challenging.When applied to solving large-scale problems,generative adversarial networks suffer from issues such as model collapse and insufficient population diversity,which lead to a decline in optimization performance.To address the above issue,this paper proposes a large-scale multi-objective optimization algorithm based on parallel weight association and guided direction-based Wasserstein generative adversarial networks with gradient penalty.The proposed algorithm employs a Wasserstein generative adversarial network with gradient penalty to learn the population distribution and rapidly generate high-quality offspring.To ensure that the model learns a more optimal population distribution,two effective strategies are designed to generate training sets that possess both convergence and diversity.Firstly,the parallel weight association strategy constructs parallel weight-associated directions in the high-dimensional decision space,focusing the search on areas with greater convergence potential,thereby enhancing population convergence.Secondly,the directional guidance strategy builds search directions that combine diversity and convergence potential to guide the population evolution,maintaining population diversity and avoiding local optima.The effectiveness of the algorithm has been verified on standard test sets and a real-world application problem.Experimental results show that the proposed algorithm signi-ficantly outperforms several current state-of-the-art large-scale multi-objective evolutionary algorithms in terms of solution set quality,providing an efficient solution for this type of problem.

Key words: Large-scale multi-objective optimization, Evolutionary algorithm, Generative adversarial network, Parallel weight association, Direction guidance