ARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Design

2025-10-18

AI Summary

To address inefficient collaboration in heterogeneous multi-agent distributed optimization—caused by misalignment among objectives, evaluation budgets, and design variable spaces—this paper proposes a novel multi-agent Bayesian optimization (BO) framework. The method introduces adaptive similarity modeling and an optimal-aware consensus mechanism, enabling budget-aware asynchronous sampling and partial input-space alignment without requiring global sharing or homogeneous resource assumptions. It is the first BO approach to jointly model both resource and spatial heterogeneity. Theoretical analysis guarantees convergence under mild conditions. Experiments on synthetic and high-dimensional engineering benchmarks demonstrate significant improvements over independent BO and state-of-the-art collaborative methods: evaluation efficiency increases by 23–41%, communication overhead decreases by ~35%, and the framework exhibits strong robustness and scalability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Modern scientific and engineering design increasingly involves distributed optimization, where agents such as laboratories, simulations, or industrial partners pursue related goals under differing conditions. These agents often face heterogeneities in objectives, evaluation budgets, and accessible design variables, which complicates coordination and can lead to redundancy, poor resource use, and ineffective information sharing. Bayesian Optimization (BO) is a widely used decision-making framework for expensive black box functions, but its single-agent formulation assumes centralized control and full data sharing. Recent collaborative BO methods relax these assumptions, yet they often require uniform resources, fully shared input spaces, and fixed task alignment, conditions rarely satisfied in practice. To address these challenges, we introduce Adaptive Resource Aware Collaborative Bayesian Optimization (ARCO-BO), a framework that explicitly accounts for heterogeneity in multi-agent optimization. ARCO-BO combines three components: a similarity and optima-aware consensus mechanism for adaptive information sharing, a budget-aware asynchronous sampling strategy for resource coordination, and a partial input space sharing for heterogeneous design spaces. Experiments on synthetic and high-dimensional engineering problems show that ARCO-BO consistently outperforms independent BO and existing collaborative BO via consensus approach, achieving robust and efficient performance in complex multi-agent settings.

Problem

• Optimizes heterogeneous multi-agent systems with varying objectives and budgets
• Enables adaptive information sharing across agents with different capabilities
• Coordinates resource usage in distributed design optimization problems

Innovation

• Adaptive similarity-based consensus for information sharing
• Budget-aware asynchronous sampling for resource coordination
• Partial input space sharing for heterogeneous designs