For complex multidisciplinary optimization (MDO) problems (e.g. preliminary aircraft design considering the disciplines of of structures, controls, aerodynamics), the cost of optimization can be quite large, especially if the optimization includes uncertainty. In computer science, multiagent systems solve complex problems by decomposing them into autonomous subtasks. A general definition posits a multiagent system to be comprised of several autonomous agents with possibly different objectives that work toward a common task. In terms of optimization, a multiagent system could solve decomposed problems such that the agents only know subproblems. In our work, we developed a methodology to approximate complex, computationally expensive RBDO problems, and use the approximated problems to efficiently find a solution with a high level of accuracy. The process took advantage of multiagent techniques. Each agent built a different surrogate and optimized it to find an optimum. In effect, this process creates surrogatebased agents. Each agent defined a different search process. Through cooperation (exchange of search points), the agents system defined highlevel methodologies whose efficiency and robustness were studied. The use of surrogates to replace expensive simulations and experiments in optimization has been well documented. To take advantage of parallel computing, many have proposed strategies for using multiple surrogates for optimization. Surrogatebased agents are related to multisurrogates, but the reference to “agents” stresses the fact that agents can change strategies at runtime and their actions are asynchronous. There are several strategies that agents may employ:

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