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The Modularly Integrated Design Assistance Suite (MIDAS) is a robust optimization tool for solving nuclear
engineering optimization problems. It was first conceived in 2018 for LWR fuel lattice optimization. Afterward,
it grew in both optimization approaches and application space as it as it expanded to PWR loading pattern
optimization problems. It adopted its current name, MIDAS, and has since been continuously developed, with
new techniques and applications added regularly. Currently, it supports optimizations for LWR fuel lattices
and core designs. MIDAS can also handle generic categorical and numerical optimization problems (devel). The
optimization suite has several algorithms currently available including traditional meta heuristic, and machine
learning techniques. MIDAS can interface with widely used reactor design tools, such as PARCS and Polaris,
and due to the modular design of the code new interfaces can be easily implemented.
MIDAS aims at allowing a flexible management of optimization algorithms, optimization problems and codes.
All classes in MIDAS are modular allowing for easy implementation of new optimization algorithms and
applications.
A unique aspect of MIDAS' architecture is that the performance evaluation of solutions and the algorithmic
generation of individuals is separated. This greatly enhances the modularity of the code as the algorithm
modules are required to perform fewer functions and a single module is needed for the performance evaluations.
To facilitate this, the input to the algorithm modules is the population of solution objects which include all
aspects of the solutions including the chromosome and affiliated fitness value and the output of the modules
are the new chromosomes themselves.
Below is the general flowchart of an optimization executed through MIDAS where the solution evaluation and the
algorithmic generation of solutions is clearly separated.
[1] B. Andersen, G. Delipei, D. Kropaczek, and J. Hou, MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems, arXiv:2204.00141, 2022
[2] G. Delipei, J. Mikouchi-Lopez, P. Rouxelin and J. Hou, Reactor Core Loading Pattern Optimization with Reinforcement Learning, The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (Accepted), 2023.
[3] J Mikouchi-Lopez, G Delipei and J Hou, Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems, Nuclear Science and Technology Open Research, 2024.
[4] J C. Luque-Gutierrez, J Mikouchi-Lopez and J Hou, Investigation on Dimension Reduction Techniques for Visualization of Reactor Core Design Space, American Nuclear Society Advances in Nuclear Fuel Management conference (accepted), 2025.
[5] J Mikouchi-Lopez, J C. Luque-Gutierrez, M Edney and J Hou, A Parametric Study of Genetic Algorithm using IPWR Core Design, American Nuclear Society Advances in Nuclear Fuel Management conference (accepted), 2025.
[6] B Andersen, Development and assessment of multi-objective optimization utilizing genetic algorithms for nuclear fuel assembly design, M.S. thesis, North Carolina State University, Department of Nuclear Engineering, 2018.
[7] B Andersen, J Hou, A Godfrey, D Kropaczek, A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models, https://doi.org/10.3390/eng3040036, 2022.
[8] K Ogujiuba, J Mikouchi-Lopez, G Delipei, J Hou, C3-LPO:A Machine Learning Based Surrogate Model for Reactor Design Evaluation and Optimization, Conference: International Conference on Physics of Reactors (PHYSOR), https://doi.org/10.3390/eng3040036, 2024.
[9] K Nguyen, G Delipei, J Kim, M Abdo, C Wang, J Hou, Deep Neural Operator Surrogate Models for Light Water Reactor Design Optimization, Conference: International Conference on Physics of Reactors (PHYSOR), https://doi.org/10.5281/zenodo.20803310, 2026.
[10] C Howard, J Hou, Design Optimization of a Heat-Pipe Microreactor Design using Bayesian Optimization, Conference: International Conference on Physics of Reactors (PHYSOR), https://doi.org/10.5281/zenodo.20803421, 2026.
[11] J Edney, N Rollins, C Parisi, R Christian, S Lawrence, J Hou, Optimization of Equilibrium Core Shuffling Scheme for Extended Power Uprate Scenarios, Conference: International Conference on Physics of Reactors (PHYSOR), https://doi.org/10.5281/zenodo.20803326, 2026.
[12] C Howard, Design Optimization of a Heat-Pipe Microreactor Using Bayesian Optimization, M.S. thesis, North Carolina State University, Department of Nuclear Engineering, 2026.
[13] N Rollins, Development of an Advanced Framework for PWR Power Uprates and Equilibrium Cycle Optimization, PhD thesis, North Carolina State University, Department of Nuclear Engineering, 2025.