Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)

Zio, Enrico; Viadana, Giorgio

doi:10.1016/j.ress.2011.06.010

Cited by 23 publications

(11 citation statements)

References 14 publications

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“…Both JADE and μJADE feature a binomial crossover method, which is the standard method for DE, and allows for any combination of mutated and non-mutated components. Alternatives include the exponential crossover method, which crosses over a number of consecutive components, but this is generally inferior to binomial crossover (Zio and Viadana, 2011).…”

Section: Jade Mojade and Moμjadementioning

confidence: 99%

“…However, greedy algorithms typically have a higher risk of losing diversity in the population. Without diversity the algorithm can prematurely converge on a solution which is a local optimum, rather than a global optimum, simply because it is unaware other possibilities exist (Zio and Viadana, 2011). DE algorithms have previously been successfully applied to nuclear reactor core design optimization problems (Sacco et al, 2009); however, they do not yet appear to have been applied to nuclear fuel assembly design optimization problems, thus making this investigation both novel and a useful step in examining DE's applicability to solving such problems.…”

Section: Introductionmentioning

confidence: 99%

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Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Charles

Parks

2019

Annals of Nuclear Energy

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Multi-objective optimization of nuclear engineering fuel assembly design problems is particularly difficult due to the highly non-linear interactions of a large number of possible variables. In addition, effective optimization algorithms are often highly problem-dependent and require extensive tuning, which reduces their applicability to the real world. To address this issue, Differential Evolution (DE) algorithms have been proposed as a new and effective method for heterogeneous fuel assembly optimization design problems. This paper presents the first complete study to investigate their applicability and performance. Firstly, two multiobjective DE algorithms have their performance compared against an Evolutionary Algorithm (EA) from the literature in optimizing a CORAIL mixed-oxide (MOX) fuel assembly for maximum plutonium content and minimum power peaking factor. Statistical analysis of the results shows the DE algorithms exhibit superior performance to the EA. The DE algorithms are then used to optimize a MOX fuel assembly with gadolinia poison, with results showing DE produces assembly designs comparable in performance to those in the literature. Finally, a sensitivity study is conducted on the control parameters of the better performing of the DE algorithms. Results indicate DE performance remains consistent for a wide range of values of both control parameters, suggesting the algorithm is able to perform effectively without requiring user expertise or effort to find the 'optimal' control parameter settings.

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Section: Jade Mojade and Moμjadementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Charles

Parks

2019

Annals of Nuclear Energy

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“…The DE is a population-based algorithm similar to GA, but with more perturbations in the iterations of the population [11]- [13].…”

Section: B Differential Evolution (De)mentioning

confidence: 99%

Availability Optimization of Parallel-Series System by Evolutionary Computation

Mellal

Zio²

2018

2018 3rd International Conference on System Reliability and Safety (ICSRS)

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This paper addresses the problem of availability optimization of a parallel-series system by using evolutionary techniques. Five techniques are considered, namely the cuckoo optimization algorithm, particle swarm optimization, flower pollination algorithm, differential evolution, and genetic algorithms. The integer values and constraints of cost are handled. The results regarding a system with ten subsystems are compared. It demonstrates that the cuckoo optimization algorithm performs best.

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“…When tackling a multi‐objective problem by GA, the various approaches to fitness definition may be distinguished into three categories [29], namely aggregation method, population‐based non‐Pareto method and Pareto‐based method. In Pareto‐based method, the chromosomes of a population are ranked according to the Pareto dominance criterion applied to the fitnesses.…”

Section: Moec Algorithmsmentioning

confidence: 99%

“…Three efficient versions of DE for multi‐objective optimisation problems were proposed by Babu et al . [29, 34]. Two of these versions work with an archive approach, but avoid the ranking procedure, while the third one, called MODE‐3, inherits the strength of the selection process scheme for single‐objective optimisation.…”

Section: Moec Algorithmsmentioning

confidence: 99%

Recurrent multi‐objective differential evolution approach for reactive power management

Singh¹,

Srivastava²

2016

IET Generation, Transmission & Distribution

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Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)

Cited by 23 publications

References 14 publications

Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Availability Optimization of Parallel-Series System by Evolutionary Computation

Recurrent multi‐objective differential evolution approach for reactive power management

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