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

Wenjie Qiu, Zixin Wang, Hongyu Fang, Zeyuan Ma, Yue-Jiao Gong

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.

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

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.

Paper Structure

This paper contains 56 sections, 53 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. LSGO and CC
  4. MetaBBO
  5. Methodology
  6. MDP Formulation
  7. Action
  8. State
  9. Reward
  10. Network Design
  11. State Embedding
  12. Actor Network
  13. Critic Network
  14. Warm-starting and Context Memory
  15. Auto-H-LSGO Benchmark
  16. ...and 41 more sections

Figures (5)

  • Figure 1: Motivation: Shifts in problem paradigms drive corresponding shifts in optimization paradigms.
  • Figure 2: The network of LH-CC
  • Figure 3: The generation pipeline for diverse problem instances within the Auto-H-LSGO.
  • Figure 4: Comparative Optimization Performance of LH-CC-G on Representative H-LSGO Problems
  • Figure 5: Ablation Study on the Action Space