Multi-objective genetic algorithm optimization of a directionally sensitive radiation detection system using a surrogate transport model

Direction-sensitive and imaging radiation detection systems often rely on complicated mask geometries to reconstruct source positions and images. Optimizing the design of these masks is difficult, often non-intuitive, and computationally intensive due to the radiation transport involved. Advances in...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 104; S. 104357
Hauptverfasser: Holland, Darren E., Olesen, Robert J., Bevins, James E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2021
Schlagworte:
Genetic algorithm
Monte Carlo radiation transport
Multi-objective optimization
Radioactive source direction
Rotating scatter mask
Surrogate model
Rotating scatter mask
Monte Carlo radiation transport
Multi-objective optimization
Radioactive source direction
Surrogate model
Genetic algorithm
ISSN:0952-1976, 1873-6769
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Zusammenfassung:Direction-sensitive and imaging radiation detection systems often rely on complicated mask geometries to reconstruct source positions and images. Optimizing the design of these masks is difficult, often non-intuitive, and computationally intensive due to the radiation transport involved. Advances in computational resources, efficient, stochastic optimization techniques, and improved variance reduction in radiation transport through hybrid deterministic-Monte Carlo methods have enabled researchers to consider methods to find formal optimal solutions for these systems. However, many systems span a complex and large design space making full radiation transport simulations to inform the optimization routine computationally intractable. This work applies a multi-objective genetic algorithm to a generalizable surrogate model, benchmarked through full Monte Carlo radiation transport simulations, to determine the optimal design for the rotating scatter mask directionally-sensitive detection system. Performing the equivalent optimization of 10,000 design evaluations with Monte Carlo radiation transport simulations would take 460 CPU-years. In contrast, the 10,000 surrogate design evaluations produced Pareto frontiers comparable to the Monte Carlo results, while reducing the computational cost by 99.998% to 4.1 CPU-days. The chosen optimal designs maintain high directional accuracy with respect to the original design, while improving the response basis similarity by 14% and increasing the efficiency by a factor of 40 at the cost of increasing the mask’s mass by a factor of 3. •Multi-objective genetic algorithm provided multiple valid Pareto designs.•Average computational cost reduced by 99.998% by using surrogate approach.•Monte Carlo and surrogate simulations produce similar optimal solutions.•Optimized design increases accuracy and efficiency, while remaining portable.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2021.104357