Multiobjective loading pattern optimization by simulated annealing employing discontinuous penalty function and screening technique
TL;DR: In this article, the problem of multiobjective fuel loading pattern (LP) optimization employing high-fidelity three-dimensional (3D) models is resolved by introducing the concepts of discontinuous penalty function.
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Abstract: The problem of multiobjective fuel loading pattern (LP) optimization employing high-fidelity three-dimensional (3-D) models is resolved by introducing the concepts of discontinuous penalty function...
read more
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Citations
Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications
TL;DR: In this paper, a rule-based RL methodology is proposed to guide evolutionary algorithms in constrained optimization, where RL proximal policy optimization agents are trained to master matching some of the problem rules/constraints, then RL is used to inject experiences to guide various evolutionary/stochastic algorithms such as genetic algorithms, simulated annealing, particle swarm optimization, differential evolution, and natural evolution strategies.
41
Multi-objective loading pattern enhancement of PWR based on the Discrete Firefly Algorithm
TL;DR: Predictors indicate that the proposed DFA has the capability and sufficient strength to obtain the near optimized loading pattern for the considered multi-objective fitness function and the reliability and efficiency have been ascertained from loading pattern optimization results.
37
An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel
Ephraim Nissan
- 15 Jul 2019
TL;DR: An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence techniques have been applied to this problem.
29
Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods
TL;DR: General-use multiobjective core reloading pattern optimization is performed using modified genetic algorithms (MGA) and it has been observed that the developed intelligent strategy performs these tasks with a reasonable computational cost.
3D in-core fuel management optimization for breed-and-burn reactors
TL;DR: In this paper, a new conceptual design of a B&B core made of axially segmented fuel assemblies was adopted to facilitate the 3D shuffling, and a Simulated Annealing (SA) algorithm was developed to automate the search for the optimal 3-dimensional shuffling pattern.
22
References
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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In-Core Nuclear Fuel Management Optimization for Pressurized Water Reactors Utilizing Simulated Annealing
TL;DR: In this article, an in-core nuclear fuel management code for pressurized water reactor reload design has been developed that combines the stochastic optimization technique of simulated annealing with a computationally efficient core physics model based on second-order accurate generalized perturbation theory.
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Dominance-Based Multiobjective Simulated Annealing
TL;DR: A multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions is proposed and a method for choosing perturbation scalings promoting search both towards and across the Pareto front is proposed.
Multiobjective Pressurized Water Reactor Reload Core Design by Nondominated Genetic Algorithm Search
TL;DR: A genetic algorithm (GA) designed to perform true multiobjective optimization on pressurized water reactor reload cores is described and it is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved.
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Optimization of Pressurized Water Reactor Shuffling by Simulated Annealing with Heuristics
TL;DR: Simulated-annealing optimization of reactor core loading patterns is implemented with support for design heuristics during candidate pattern generation and the SIMAN optimization module uses the advan...
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