Application of a hybrid method based on the combination of genetic algorithm and Hopfield neural network for burnable poison placement

Khoshahval, Farrokh; Fadaei, Amir Hosein

doi:10.1016/j.anucene.2012.04.020

Cited by 13 publications

(2 citation statements)

References 9 publications

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“…Ref. [18] successfully integrated the usage of GA with HNN for solving combinatorial optimization problems. In their study, the HNN was applied to GA to minimize each other's shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

et al. 2022

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Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.

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Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

et al. 2022

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“…Additionally, they were also used in the fi eld of nuclear engineering. In the available literature, loading pattern [1], core design [5,6], BA arrangement [7,8], online refueling [9], and thorium loading [10] were considered. However, due to the required computing power, these optimizations were typically simplifi ed and focused on one parameter or used signifi cantly simplifi ed models.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

2021

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The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (k eff), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.

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A new method for multi-objective in core fuel management optimization using biogeography based algorithm

Khoshahval

Zolfaghari

Minuchehr

2014

Annals of Nuclear Energy

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Application of a hybrid method based on the combination of genetic algorithm and Hopfield neural network for burnable poison placement

Cited by 13 publications

References 9 publications

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

A new method for multi-objective in core fuel management optimization using biogeography based algorithm

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