Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems
This paper investigates the use of Multi-Task Bayesian Optimization for tuning decentralized trajectory generation algorithms in multi-drone systems. We treat each task as a trajectory generation scenario defined by a specific number of drone-to-drone interactions. To model relationships across scenarios, we employ Multi-Task Gaussian Processes, which capture shared structure across tasks and enable efficient information transfer during optimization. We compare two strategies: optimizing the average mission time across all tasks and optimizing each task individually. Through a comprehensive simulation campaign, we show that single-task optimization leads to progressively shorter mission times as swarm size grows, but requires significantly more optimization time than the average-task approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Gaussian ProcessesSimilar Papers 제목 키워드 기반
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-…
Code GenerationPoint TrackingModel Fusion through Bayesian Optimization in Language Model Fine-Tuning
Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troubles…
Bayesian OptimizationLanguage ModelingLanguage ModellingmodelDECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data
Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often distributed across multiple clients, decentralized fine-tuning offers…
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization
Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a computationally cheap surrogate model is em…
Bayesian OptimizationComputational EfficiencyHyperparameter OptimizationCATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of st…
Bayesian OptimizationBenchmarkingCompiler Optimizationtensor algebra