Graduate Thesis Or Dissertation

A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

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  • We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be solved in a modular approach, where agents can be added to explore different regions of the design space, organize explored designs, and develop the Pareto front (PF). Using these methods, we created a generalized optimization methodology which can be used to solve a variety of optimization problems. The MABS was applied to a variety of multi-objective optimization engineering benchmarks, and we found that it can outperform many other multi-objective optimization algorithms. We also found that the MABS is highly parallelizable due to the advent of using multiple agents, which allows us to reduce the time required to converge on the PF. The MABS was also applied to a series of sodium fast reactor (SFR) design problems including the creation of a plutonium burner core, placement of experimental assemblies in a test reactor, and the proliferation resistance of a generalized SFR.
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