Surrogate-based agents for constrained optimization
Résumé
Multi-agent systems have been used to solve complex problems by decomposing them into autonomous subtasks. Drawing inspiration from both multi-surrogate and multi-agent techniques, we de ne in this article optimization subtasks that employ di erent approxi- mations of the data in subregions through the choice of surrogate, which creates surrogate- based agents. We explore a method of design space partitioning that assigns agents to subregions of the design space, which drives the agents to locate optima through a mixture of optimization and exploration in the subregions. These methods are illustrated on two constrained optimization problems, one with uncertainty and another with small, discon- nected feasible regions. It is observed that using a system of surrogate-based optimization agents is more e ective at locating the optimum compared to optimization with a single surrogate over the entire design space.