A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization

2026-03-29

AI Summary

This work addresses the challenge in heterogeneous large-scale global optimization (H-LSGO), where substantial differences in subproblem dimensionality and landscape characteristics render conventional cooperative coevolutionary methods ineffective. To this end, the paper introduces LH-CC, the first learning-based heterogeneous cooperative coevolution framework. LH-CC formulates the optimization process as a Markov decision process and employs a meta-agent to dynamically select the most suitable solver, enabling adaptive collaborative optimization of heterogeneous subproblems. The framework synergistically integrates reinforcement learning, meta-learning, and cooperative coevolution, supporting ensemble-based optimizer integration and adaptive scheduling. A flexible H-LSGO benchmark suite is also developed. Experimental results demonstrate that LH-CC significantly outperforms state-of-the-art methods on 3000-dimensional strongly coupled problems, achieving notable advances in solution quality, computational efficiency, and cross-problem generalization capability.

Technology Category

Search and Optimization: Learning to SearchIntelligent Robots: Learning & Optimization for ROBConstraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd workSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Cooperative Coevolution (CC) effectively addresses Large-Scale Global Optimization (LSGO) via decomposition but struggles with the emerging class of Heterogeneous LSGO (H-LSGO) problems arising from real-world applications, where subproblems exhibit diverse dimensions and distinct landscapes. The prevailing CC paradigm, relying on a fixed low-dimensional optimizer, often fails to navigate this heterogeneity. To address this limitation, we propose the Learning-Based Heterogeneous Cooperative Coevolution Framework (LH-CC). By formulating the optimization process as a Markov Decision Process, LH-CC employs a meta-agent to adaptively select the most suitable optimizer for each subproblem. We also introduce a flexible benchmark suite to generate diverse H-LSGO problem instances. Extensive experiments on 3000-dimensional problems with complex coupling relationships demonstrate that LH-CC achieves superior solution quality and computational efficiency compared to state-of-the-art baselines. Furthermore, the framework exhibits robust generalization across varying problem instances, optimization horizons, and optimizers. Our findings reveal that dynamic optimizer selection is a pivotal strategy for solving complex H-LSGO problems.

Problem

• Heterogeneous Large-Scale Global Optimization
• Cooperative Coevolution
• Subproblem Heterogeneity
• Optimizer Selection
• Markov Decision Process

Innovation

• Learning-Based Optimization
• Heterogeneous Large-Scale Global Optimization
• Cooperative Coevolution
• Meta-Agent
• Markov Decision Process