A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions
This addresses the need for multiple optimal solutions in real-world applications where practical constraints may limit the feasibility of a single global optimum, enabling quick switching to alternative solutions to maintain system performance.
The paper tackles the problem of finding multiple local and global optima in expensive-to-evaluate multimodal functions, developing a Bayesian optimization framework that extends acquisition functions to locate a set of solutions, with performance demonstrated on multiple optimization problems.
Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient optimization in domains with expensive function evaluations. The existing BO techniques are capable of finding a single global optimum solution. However, finding a set of global and local optimum solutions is crucial in a wide range of real-world problems, as implementing some of the optimal solutions might not be feasible due to various practical restrictions (e.g., resource limitation, physical constraints, etc.). In such domains, if multiple solutions are known, the implementation can be quickly switched to another solution, and the best possible system performance can still be obtained. This paper develops a multimodal BO framework to effectively find a set of local/global solutions for expensive-to-evaluate multimodal objective functions. We consider the standard BO setting with Gaussian process regression representing the objective function. We analytically derive the joint distribution of the objective function and its first-order derivatives. This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process. We introduce variants of the well-known BO acquisition functions to the multimodal setting and demonstrate the performance of the proposed framework in locating a set of local optimum solutions using multiple optimization problems.