Bat algorithm for the fuel arrangement optimization of reactor core

Kashi, Samira; Minuchehr, A.; Poursalehi, N.; Zolfaghari, A.

doi:10.1016/j.anucene.2013.09.044

Cited by 44 publications

(9 citation statements)

References 31 publications

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“…The evolution of nature Genetic algorithm [129], differential evolution algorithm [130] Human behaviour Immune algorithm [131], brainstorm algorithm [132], fireworks algorithm [133] Physical characteristics Simulated annealing algorithm [134], intelligent water drop algorithm [135], binary black hole algorithm [136] Animal behaviour Particle swarm optimization algorithm [137], ant algorithm [138], bat algorithm [139], krill herd algorithm [140], whale optimization algorithm [141]…”

Section: Imitated Objectmentioning

confidence: 99%

Challenges and Prospects of Digital Core-Reconstruction Research

et al. 2019

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The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.

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Section: Imitated Objectmentioning

confidence: 99%

Challenges and Prospects of Digital Core-Reconstruction Research

et al. 2019

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“…The bat algorithm has been extended to multiobjective optimization and hybrid versions as well as chaotic bat algorithm with many applications [39,35,26,56,16].…”

Section: Algorithms Based On Swarm Intelligencementioning

confidence: 99%

Swarm intelligence: past, present and future

Yang¹,

Deb

Zhao

et al. 2017

Soft Comput

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Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

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“…It is also one of the most challenging problems in nuclear reactor engineering, and has not been solved well yet, Gong et al (2011). There are many optimization methods and papers about LPO during recent years, such as ant colony algorithm (Machado and Schirru, 2002), artificial intelligence techniques like artificial neural networks (Sadighi et al, 2002), genetic algorithms (Mohseni et al, 2008), discrete particle swarm optimization (Babazadeh et al, 2009), continuous particle swarm optimization (Meneses et al, 2009;Babazadeh et al, 2009), improved pivot particle swarm method (Liu and Cai, 2012), interval bound algorithm (Gong et al, 2011), harmony search algorithm (Poursalehi et al, 2013a), continuous firefly algorithm (Poursalehi et al, 2013b), discrete firefly algorithm (Poursalehi et al, 2013c), bat algorithm (Kashi et al, 2014) and etc.…”

Section: Introductionmentioning

confidence: 99%

Bat algorithm for the fuel arrangement optimization of reactor core

Cited by 44 publications

References 31 publications

Challenges and Prospects of Digital Core-Reconstruction Research

Challenges and Prospects of Digital Core-Reconstruction Research

Swarm intelligence: past, present and future

Development of a new approach, evolutionary harmony search algorithm, for the LPO problem

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