Multicycle Fuel Loading Pattern Optimization by Multiobjective Simulated Annealing Employing Adaptively Constrained Discontinuous Penalty Function

Park, Tong Kyu; Joo, Han Gyu; Kim, Chang Hyo

doi:10.13182/nse12-41

Cited by 12 publications

(1 citation statement)

References 2 publications

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“…Martin-Del-Campo and other researchers also developed an initial GA and then applied the lessons learned in their first algorithm to develop an updated GA [18,19]. Similarly, while developing their specialized objective function, Park et al updated their parallel SA algorithm to improve on issues encountered in their optimization and objective function [20,21]. Some studies have also compared many different optimization algorithms to test their pin lattice and core loading pattern optimization performance [22,23].…”

Section: Introductionmentioning

confidence: 99%

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems

Andersen¹,

Delipei²,

Kropaczek³

et al. 2022

Preprint

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A variety of optimization algorithms have been developed to solve engineering design problems in which the solution space is too large to manually determine the optimal solution. The Modular Optimization Framework (MOF) was developed to facilitate the development and application of these optimization algorithms. MOF is written in Python 3, and it uses object-oriented programming to create a modular design that allows users to easily incorporate new optimization algorithms, methods, or engineering design problems into the framework.Additionally, a common input file allows users to easily specify design problems, update the optimization parameters, and perform comparisons between various optimization methods and algorithms. In the current MOF version, genetic algorithm (GA) and simulated annealing (SA) approaches are implemented. Applications to different nuclear engineering optimization problems are included as examples. The effectiveness of the GA and SA optimization

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