Optimal parameter estimation for
            <scp>PEMFC</scp>
            using modified monarch butterfly optimization

Yuan, Zhi; Wang, Weiqing; Wang, Haiyun

doi:10.1002/er.5527

Cited by 26 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ending by assuming that the movement to the next generation of the MB individuals, having the best fitness, is automatically performed and there is no operator can change them. Hence, this will prevent any deterioration for the MB population and maintain the effectiveness of the population while increasing the generations [129,130]. However, a major drawback of MBO is that it sometimes gets trapped into local optimum which leads to premature convergence.…”

Section: Monarch Butterfly Optimizer (Mbo)mentioning

confidence: 99%

“…However, a major drawback of MBO is that it sometimes gets trapped into local optimum which leads to premature convergence. As a result, a modified MBO (MMBO) has been utilized in [130] to solve this issue. In MMBO, two mechanisms have been integrated with the basic MBO where the first is the mutation operator and the second is the anti-cosine operator, While the procedures of MMBO are revealed in Fig.…”

Section: Monarch Butterfly Optimizer (Mbo)mentioning

confidence: 99%

“…5. Accordingly, MMBO has been utilized for tackling the parameter estimation problem of the PEMFCs, while the results are depicted in Table 7 in the eleventh row [130].…”

Section: Monarch Butterfly Optimizer (Mbo)mentioning

confidence: 99%

See 2 more Smart Citations

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

show abstract

Section: Monarch Butterfly Optimizer (Mbo)mentioning

confidence: 99%

Section: Monarch Butterfly Optimizer (Mbo)mentioning

confidence: 99%

See 1 more Smart Citation

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…It has strong versatility and can solve various continuous problems as well as discrete problems. [3][4][5] Up to now, there have been many mature metaheuristic algorithms with good performance, such as Sparrow Search Algorithm (SSA), [6][7][8] Genetic Algorithm (GA), [9][10][11] Butterfly Optimization Algorithm (BOA), [12][13][14] Differential Evolution (DE), [15][16][17][18] Cuckoo Search (CS), [19][20][21] Harris Hawk Optimization (HHO), [22][23][24] Gray Wolf Optimization (GWO), [25][26][27][28] Fish Migration Optimization (FMO), 29,30 Particle Swarm Optimization (PSO), [31][32][33][34] Phasmatodea Population Evolution algorithm (PPE), 35,36 Cat Swarm Optimization (CSO), [37][38][39] and Ant Colony Optimization (ACO) [40][41][42][43] .…”

Section: Introductionmentioning

confidence: 99%

A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

Wang

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The Fruit Fly Optimization Algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization Algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure—Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm. The new algorithm uses the evolution matrix in QUasi-Affine TRansformation Evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm can achieve better vehicle routes planning.

show abstract

“…28 Rao et al applies artificial bee colony (ABC) algorithm to solve the distribution system loss minimization problem. 29 Yuan et al 30 uses monarch butterfly optimization (MBO) to optimize the parameters for polymer electrolyte membrane fuel cells. Wang et al proposed a new intelligent algorithm named as differential evolution with a new encoding mechanism (DEEM) for optimizing the layout of the wind field.…”

Section: Introductionmentioning

confidence: 99%

A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

Wang

et al. 2021

Int J Energy Res

View full text Add to dashboard Cite

Residual hydrogenation fractionation process (RHFP) is significant for extracting valuable fractions from heavy oil with a severe energy consumption. The operational optimization of RHPF for comprehensively improving product profits and reducing energy consumption costs has been studied in this paper. Considering the complex nonlinear dynamic constraints, a novel neighborhood-adaptive state transition algorithm (NaSTA) is proposed. Specifically, a variable localneighborhood strategy is designed to adaptively change the size of the neighborhood and speed up the initial search. Then, randomly generating large-scope candidate solutions aids the algorithm to jump out of the local optimal solution.In addition, an out-of-bound candidate solution processing mechanism is proposed to reasonably allocate candidate solutions that exceed the predefined neighborhood boundaries. The statistical research manifests the superiority of the proposed method compared with other optimization algorithms. The experimental results verify that the energy consumption is considerably reduced by approximately 10.06% so that the overall profit is improved.

show abstract

Optimal parameter estimation for PEMFC using modified monarch butterfly optimization

Cited by 26 publications

References 37 publications

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

Contact Info

Product

Resources

About