Bayesian optimization with local search

TL;DR

This paper introduces a novel multi-start global optimization method that uses Bayesian optimization to select starting points by constructing a new function through local searches, aiming to efficiently find the original function's global optima.

Contribution

The paper proposes a new multi-start optimization algorithm that integrates Bayesian optimization with local search to improve global optimization performance.

Findings

Demonstrates effectiveness on benchmark problems

Achieves better convergence than traditional methods

Reduces the number of local searches needed

Abstract

Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple starting points. In this work we propose a new multi-start algorithm where the starting points are determined in a Bayesian optimization framework. Specifically, the method can be understood as to construct a new function by conducting local searches of the original objective function, where the new function attains the same global optima as the original one. Bayesian optimization is then applied to find the global optima of the new local search defined function.