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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| Published in: | Engineering applications of artificial intelligence Vol. 104; p. 104357 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.09.2021
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 104357 |
| Author | Olesen, Robert J. Holland, Darren E. Bevins, James E. |
| Author_xml | – sequence: 1 givenname: Darren E. surname: Holland fullname: Holland, Darren E. email: Darren.Holland@afit.edu – sequence: 2 givenname: Robert J. surname: Olesen fullname: Olesen, Robert J. – sequence: 3 givenname: James E. orcidid: 0000-0001-6140-199X surname: Bevins fullname: Bevins, James E. |
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| Keywords | Rotating scatter mask Monte Carlo radiation transport Multi-objective optimization Radioactive source direction Surrogate model Genetic algorithm |
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| SubjectTerms | Genetic algorithm Monte Carlo radiation transport Multi-objective optimization Radioactive source direction Rotating scatter mask Surrogate model |
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