2023/03/25 by Dinesh Krishnamoorthy, Krishnamoorthy, Dinesh, Joel A. Paulson +1 · 1 citation
Computer Science · Engineering · #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning and Algorithms #Metaheuristic Optimization Algorithms Research #Multiagent Systems (cs.MA) #Optimization and Control (math.OC) #Parallel #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2303.14414
openalex publication_date 2023/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for coordinated decision-making in multi-agent systems with unknown structure has not been widely studied. This paper investigates a black-box optimization problem over a multi-agent network coupled via shared variables or constraints, where each subproblem is formulated as a BO that uses only its local data. The proposed multi-agent BO (MABO) framework adds a penalty term to traditional BO acquisition functions to account for coupling between the subsystems without data sharing. We derive a suitable form for this penalty term using alternating directions method of multipliers (ADMM), which enables the local decision-making problems to be solved in parallel (and potentially asynchronously). The effectiveness of the proposed MABO method is demonstrated on an intelligent transport system for fuel efficient vehicle platooning.