Surrogate-Based Agents for Constrained Optimization

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 dene in this article optimization subtasks that employ dierent approximations of the data in subregions through the choice of surrogate, which creates surrogatebased 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, disconnected feasible regions. It is observed that using a system of surrogate-based optimization agents is more eective at locating the optimum compared to optimization with a single surrogate over the entire design space.

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