A new method for optimal parameters identification of a PEMFC using an improved version of Monarch Butterfly Optimization Algorithm

Bao, Songjian; Ebadi, Abdol Ghaffar; Toughani, Mohsen; Dalle, Juhriyansyah; Maseleno, Andino; Baharuddin, Baharuddin; Yıldızbaşı, Abdullah

doi:10.1016/j.ijhydene.2020.04.256

Cited by 52 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, with the environment becoming more complex, the application of other intelligent methods in ship collision avoidance will be tried. We aim at improving the algorithm, like monarch butterfly optimization [ 27 ], earthworm optimization algorithm [ 28 ], elephant herding optimization [ 29 ], moth search algorithm [ 30 ], slime mould algorithm [ 31 ], hunger games search [ 32 ], Runge Kutta optimizer [ 33 ], colony predation algorithm [ 34 ], and Harris hawks optimization [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Ship-Overtaking Situation Based on Swarm Intelligence Improved KDE

Han

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper proposes a data-driven risk assessment model for ship overtaking based on the particle swarm optimization (PSO) improved kernel density estimation (KDE). By minimizing the mean square error between the real probability distribution of the ship overtaking point and the kernel density estimation probability distribution calculated by the current kernel density bandwidth, the longitude and latitude of the ship overtaking point are displayed by the color corresponding to the probability as the cost objective function of the search bandwidth of the algorithm. This can better show the distribution of the overtaking points of channel propagation traffic flow. A probability-based ship-overtaking risk evaluation model is developed through the bandwidth and density analysis optimized by an intelligent algorithm. In order to speed up searching the optimal variable width of the kernel density estimator for ship encountering positions, an improved adaptive variable-width kernel density estimator is proposed. The latter reduces the risk of too smooth probability density estimation phenomenon. Its convergence is proved. Finally, the model can efficiently evaluate the risk status of ship overtaking and provide navigational auxiliary decision support for pilots.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Ship-Overtaking Situation Based on Swarm Intelligence Improved KDE

Han

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Intelligent optimization algorithms are usually used for fuel cell parameters identification 9,42 . Genetic algorithm (GA) is a method for obtaining the optimal solution according to the laws of biological evolution in nature 43 .…”

Section: Mathematic Methodologymentioning

confidence: 99%

Two parameters identification for polarization curve fitting of PEMFC based on genetic algorithm

Shen

Yan

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

“…The parameter σ mem is the specific resistance of the membrane (Ω • cm), A is the single cell active surface in cm 2 , l is the membrane thickness in (cm). The following expression for the specific resistance is used [34,36]:…”

Section: The Ohmic Lossesmentioning

confidence: 99%

Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

et al. 2022

View full text Add to dashboard Cite

This paper presents a comparison of optimizers for tuning a fractional-order proportional-integral-derivative (FOPID) and proportional-integral-derivative (PID) controllers, which were applied to a DC/DC boost converter. Grey wolf optimizer (GWO) and extended grey wolf optimizer (EGWO) have been chosen to achieve suitable parameters. This strategy aims to improve and optimize a proton exchange membrane fuel cell (PEMFC) output power quality through its link with the boost converter. The model and controllers have been implemented in a MATLAB/SIMULINK environment. This study has been conducted to compare the effectiveness of the proposed controllers in the transient, accuracy in tracking the reference current, steady-state, dynamic responses, overshoots, and response time. Results showed that the combination EGWO-FOPID had significant advantages over the rest of the optimized controllers.

show abstract

A new method for optimal parameters identification of a PEMFC using an improved version of Monarch Butterfly Optimization Algorithm

Cited by 52 publications

References 32 publications

Assessment of Ship-Overtaking Situation Based on Swarm Intelligence Improved KDE

Assessment of Ship-Overtaking Situation Based on Swarm Intelligence Improved KDE

Two parameters identification for polarization curve fitting of PEMFC based on genetic algorithm

Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

Contact Info

Product

Resources

About