General-Purpose Co-Evolutionary Construction of Parallel Algorithm Portfolios for Multi-Objective Binary Optimization

2026-05-15

AI Summary

This work addresses the lack of a general framework for constructing parallel algorithm ensembles tailored to multi-objective binary optimization problems. It proposes DACMO, a domain-agnostic coevolutionary framework that decouples domain-invariant structures from instance-specific features through neural representations and, for the first time, leverages large language models (LLMs) to automatically generate optimization operators. This approach enables operator-level design of generalizable parallel algorithms without human intervention. Evaluated across four classes of multi-objective binary optimization problems, DACMO outperforms ensembles built upon classical MOEA templates and surpasses state-of-the-art baselines—those relying on handcrafted instance generators—on two of these problem classes.

Abstract

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.

Problem

• multi-objective binary optimization
• parallel algorithm portfolios
• general-purpose construction
• algorithm design
• combinatorial optimization

Innovation

• neural instance representation
• LLM-based operator generation
• co-evolutionary algorithm
• parallel algorithm portfolios
• multi-objective binary optimization