Enhanced Global Optimization with Parallel Global and Local Structures

TL;DR

This paper introduces a parallel global optimization framework combining direct search and Bayesian methods, improving efficiency in optimizing complex functions with multiple local minima.

Contribution

It presents a novel parallel global and local search framework with proven convergence properties, enhancing optimization of complex, real-time control system functions.

Findings

Framework demonstrates superior empirical performance

Proven asymptotic convergence guarantees

Effective in complex, multi-minima functions

Abstract

In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with Bayesian parallel optimization. It consists of an iterative global and local search that searches broadly through the entire global space for promising regions and then efficiently exploits each local promising region. We prove the asymptotic convergence properties of the proposed framework and conduct an extensive numerical comparison to illustrate its empirical performance.