A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

Mahmoudi, S.M.; Aghaie, M.; Bahonar, M.; Poursalehi, N.

doi:10.1016/j.anucene.2016.04.035

Cited by 36 publications

(4 citation statements)

References 32 publications

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“…Figure 5, shows the general process flowchart of the GSA technique and the arrangement of the procedure expressed as the follows, all agents haphazardly introduced. Each agent considered as an applicant arrangement after initialization, Gravitational power, gravitational steady, and resultant powers among specialists are determined, from that point forward, the increasing speeds of particles are characterized [23]. In every cycle and the best solution so far will be updated, then the velocities of all agents will be determined with the position as well finally the process of updating velocities and positions will be stopped by meeting an end criterion [24,25]…”

Section: Psogsa Algorithmmentioning

confidence: 99%

Optimization of PID for industrial electro-hydraulic actuator using PSOGSA

et al. 2019

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The Electro-hydraulic actuator (EHA) systems known to be extremely nonlinear due to its dynamic characteristics and these existing nonlinearities and uncertainties yield to the constraint in the control of EHA system, which influences the position tracking accuracy and affect the occurrences of leakage and friction in the system. The purpose of this work is to develop the mathematical model for the industrial electrohydraulic actuator, then to design a controller by proportional-integral-derivative (PID) and optimize the parameters using Particle Swarm Optimization-Gravitational Search Algorithm (PSOGSA). A few controllers such as conventional PID (CPID) and model reference adaptive control (MRAC) designed for comparison. The performance of PID, PID-PSOGSA and modern controller MRAC will be compared in order to determine the most efficient controller. Despite all controllers are capable to provide good performance, PID-PSOGSA control methods generate good response compared to PID and MRAC in term of positioning.

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Section: Psogsa Algorithmmentioning

confidence: 99%

Optimization of PID for industrial electro-hydraulic actuator using PSOGSA

et al. 2019

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“…In recent years, some new optimization methods have appeared. On the one hand, new optimization algorithms continue to emerge, including the hybrid algorithm, 16 gravitational search algorithm (GSA), 17 teaching‐learning based optimization algorithm, 18 etc., which own better efficiency and accuracy compared with the previous ones. On the other hand, with the rapid development of computer technology and the successful coupling of various advanced analysis tools, “overall optimization” seems more feasible and reliable.…”

Section: Introductionmentioning

confidence: 99%

Automated core design code development for a lead‐cooled fast reactor and its core optimization

et al. 2020

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Summary Based on the favorable neutronic performance, T/H (thermal‐hydraulic) characteristics and inherent safety of the lead‐cooled fast reactor (LFR), University of Science and Technology of China performed a preliminary design of the 1000Mth medium‐power modular lead‐cooled fast reactor M2LFR‐1000. To solve the core optimization problem of M2LFR‐1000, an automatic core design code named CODOPT (core design and optimization) was developed, considering geometry, neutronics and thermal‐hydraulics. CODOPT starts from setting a number of the core design schemes, then evaluates their key neutronic and T/H characteristics automatically and finally finds the design schemes satisfying the design constraints. Using this code, the sensitivity analysis of the key design parameters (fuel rod diameter, pitch diameter ratio, and inlet coolant velocity) is carried out. Under the neutronic and T/H design constraints, the optimization problem of the core has been studied using a multi‐objective function, which tries to find an optimal design with larger Keff, flatter power distribution, and minimum temperature of cladding and pellet. Current results show that the overall neutronic and T/H performance of M2LFR‐1000 can be improved by choosing the design parameters presented in this paper. The automatic core design ideas adopted currently can also provide some reference for future LFR designs.

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“…A closer look does however reveal that this third type of metaheuristic is widely represented as well [35]. Two examples of this type are the Gravitational Search Algorithm [36,37] and the Big Bang-Big Crunch Algorithm [38]. Finally, a rising trend of hybrid metaheuristics, aiming an improved performance by combining components from different metaheuristics, can be observed [39,40].…”

Section: Introductionmentioning

confidence: 99%

Inverse hygric property determination based on dynamic measurements and swarm-intelligence optimisers

Vereecken

Roels

Janssen

2018

Building and Environment

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To accelerate the hygric characterisation of porous building materials, dynamic flow and storage measurements in combination with inverse parameter estimation show a lot of promise. Therein though, the processing and interpretation of the experimental output can be challenging. This paper demonstrates the applicability of two swarmintelligence (SI) optimisers, i.e. the Particle Swarm Optimiser (PSO) and the Grey Wolf Optimiser (GWO), for determining the vapour resistance factor and the sorption isotherm of porous building materials. The methodology is presented for a fictitious dynamic vapour sorption experiment on a calcium silicate insulation sample. The identifiability of the unknown parameters and the reliability of the estimated properties is investigated via a profile likelihood (PL) analysis. By use of the proposed methodology, the measurement time required to determine the hygric properties is reduced strongly compared to standard techniques such as the steady-state cup and sorption tests, and at the same time the uncertainty propagated in the parameter estimation can be characterised. A close agreement with the target values is obtained.Though, to avoid an unreliable parameter estimation it is recommended to not limit the optimisation process to a single run. Furthermore, also the results obtained during the PL analysis can help improving the estimation. Finally, for the current case study, the SI optimisers are found to outperform the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

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A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

Cited by 36 publications

References 32 publications

Optimization of PID for industrial electro-hydraulic actuator using PSOGSA

Optimization of PID for industrial electro-hydraulic actuator using PSOGSA

Automated core design code development for a lead‐cooled fast reactor and its core optimization

Inverse hygric property determination based on dynamic measurements and swarm-intelligence optimisers

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