Despite recent progress in constructing generalizable parallel algorithm portfolios (PAPs), no general-purpose approach is yet available for multi-objective binary optimization problems (MOBOPs). To fill this gap, this paper proposes domain-agnostic co-evolution of parameterized search for multi-objective binary optimization~(DACMO), which features two technical innovations. First, we propose a neural instance representation architecture that decouples domain-invariant and instance-specific features, enabling class-consistent instance generation across varying dimensions without problem-specific instance generators. Second, we introduce LLM-based automatic search operator generation into PAP construction, extending the search space from parameter tuning of predefined templates to operator-level algorithm design. We evaluate DACMO on four representative MOBOP classes to demonstrate its effectiveness as a general-purpose PAP construction method: the multi-objective match max problem~(MMMP), the multi-objective knapsack problem~(MKP), the multi-objective contamination control problem (MCCP), and the multi-objective complementary influence maximization problem~(MCIMP). Experimental results show that DACMO can be directly applied to all four problem classes without modification, outperforms PAPs built from classic MOEA templates, and achieves performance comparable to a privileged state-of-the-art baseline that relies on manually designed problem-specific instance generators, while outperforming it on two of the four evaluated problem classes.

翻译：尽管近期在构建可泛化的并行算法组合(PAPs)方面取得了进展，但目前仍缺乏针对多目标二元优化问题(MOBOPs)的通用方法。为填补这一空白，本文提出领域无关的多目标二元优化参数化搜索协同进化框架(DACMO)，其包含两项技术创新。首先，我们提出一种神经实例表示架构，该架构解耦了领域不变特征与实例特定特征，无需针对特定问题的实例生成器即可在不同维度下生成类一致性实例。其次，我们将基于大语言模型(LLM)的自动搜索算子生成引入PAP构建，将搜索空间从预定义模板的参数调优扩展至算子级算法设计。我们在四类代表性MOBOP上评估DACMO作为通用PAP构建方法的有效性：多目标最大匹配问题(MMMP)、多目标背包问题(MKP)、多目标污染控制问题(MCCP)以及多目标互补影响力最大化问题(MCIMP)。实验结果表明，DACMO可直接应用于全部四类问题而无需修改，其性能优于基于经典多目标进化算法模板构建的PAP，并达到与依赖人工设计特定实例生成器的特权基线相当的性能，且在四类问题中的两类上超越该基线。