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Derivative-free Tree Optimization for Complex Systems by Wei, Ye; Peng, Bo; Xie, Ruiwen; Chen, Yangtao; Qin, Yu; Wen, Peng; Bauer, Stefan; Tung, Po-Yen is a scholarly article available to read on EtoBox.

What is Derivative-free Tree Optimization for Complex Systems about?

A tremendous range of design tasks in materials, physics, and biology can be formulated as finding the optimum of an objective function depending on many parameters without knowing its closed-form expression or the derivative. Traditional derivative-free optimization techniques often rely on strong assumptions about objective functions, thereby failing at optimizing non-convex systems beyond 100 dimensions. Here, we present a tree search method for derivative-free optimization that enables accelerated optimal design of high-dimensional complex systems. Specifically, we introduce stochastic tree expansion, dynamic upper confidence bound, and short-range backpropagation mechanism to evade local optimum, iteratively approximating the global optimum using machine learning models. This development effectively confronts the dimensionally challenging problems, achieving convergence to global optima across various benchmark functions up to 2,000 dimensions, surpassing the existing methods by 10- to 20-fold. Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms

Author

Wei, Ye; Peng, Bo; Xie, Ruiwen; Chen, Yangtao; Qin, Yu; Wen, Peng; Bauer, Stefan; Tung, Po-Yen

Published

2024

Language

EN