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1. Byzantine-Resilient Multi-Agent Optimization

   https://doi.org/10.1109/TAC.2020.3008139  

   Su, L; Vaidya, N.H. (January 2021, IEEE transactions on automatic control)

   null (Ed.)

   We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function fi , and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantine-resilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is very challenging. For example, in contrast to fault-free networks, reaching Byzantine-resilient agreement even in the simplest setting is far from trivial. We take a step toward solving the proposed Byzantine-resilient multiagent optimization problem by focusing on scalar local cost functions. Our results might provide useful insights for the general local cost functions. 

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2. Multi-Layer Continuum Deformation Optimization of Multi-Agent Systems

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   Uppaluru, Harshvardhan; Rastgoftar, Hossein (January 2023, IFAC-PapersOnLine)
3. Multi-information source constrained Bayesian optimization

   https://doi.org/10.1007/s00158-018-2115-z  

   Ghoreishi, Seyede Fatemeh; Allaire, Douglas (March 2019, Structural and Multidisciplinary Optimization)
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5. Asynchronous Multi-Information Source Bayesian Optimization

   https://doi.org/10.1115/1.4065064  

   Khatamsaz, Danial; Arroyave, Raymundo; Allaire, Douglas L (October 2024, Journal of Mechanical Design)

   Abstract Resource management in engineering design seeks to optimally allocate while maximizing the performance metrics of the final design. Bayesian optimization (BO) is an efficient design framework that judiciously allocates resources through heuristic-based searches, aiming to identify the optimal design region with minimal experiments. Upon recommending a series of experiments or tasks, the framework anticipates their completion to augment its knowledge repository, subsequently guiding its decisions toward the most favorable next steps. However, when confronted with time constraints or other resource challenges, bottlenecks can hinder the traditional BO’s ability to assimilate knowledge and allocate resources with efficiency. In this work, we introduce an asynchronous learning framework designed to utilize idle periods between experiments. This model adeptly allocates resources, capitalizing on lower fidelity experiments to gather comprehensive insights about the target objective function. Such an approach ensures that the system progresses uninhibited by the outcomes of prior experiments, as it provisionally relies on anticipated results as stand-ins for actual outcomes. We initiate our exploration by addressing a basic problem, contrasting the efficacy of asynchronous learning against traditional synchronous multi-fidelity BO. We then employ this method to a practical challenge: optimizing a specific mechanical characteristic of a dual-phase steel. 

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