Model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal Optimization

Rezaie, Mehrdad; Azar, Keyvan Karamnejadi; sani, Armin kardan; Akbari, Ehsan; Ghadimi, Noradin; Razmjooy, Navid; Ghadamyari, Mojtaba

doi:10.1016/j.seta.2022.102657

Cited by 36 publications

(18 citation statements)

References 28 publications

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“…GJO-JOS slightly high loss with WHO on 1 f in 10, 30, and ). In higher dimensions (30,50, and 100 dimensions), GJO-JOS clearly shows a dominant result among its competitors, as exhibited in Tables 9-11.…”

Section: ) Statistical Analysis Of Gjo-jos With Seven Variant Algorit...mentioning

confidence: 86%

“…Second, the middle scores of 27.90 and 18.48 are achieved by HHO and AOS. The higher scores are obtained by GJO-JOS (47.69), WHO (44.04), AO (46.96), and ABC (50). It means these algorithms produce considerable results of mean best fitness value.…”

Section: Table 8 Convergence Curve Of Gjo-jos Vs Six Variant Oppositi...mentioning

confidence: 97%

“…In this paper, we employ JOS (the joint of DO and SLO) to enhance the Golden Jackal Optimization (GJO). GJO mimics the golden jackal's collaborative hunting behavior, which consists of three phases: prey searching, enclosing, and pounching [48], and also shows the efficacy in the applications [49], [50]. JOS assists the GJO by attacking the prey expeditiously using SLO.…”

Section: Introductionmentioning

confidence: 99%

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Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

2022

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This paper presents the logical relationships of Aristotle's square of opposition on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by joining two opposition strategies of Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal Optimization (GJO) with Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluate the performance of GJO-JOS in a competition of CEC 2017 on a set of 29 benchmark functions. The benchmark functions include unimodal, multimodal, hybrid, and composition. Based on these benchmark functions, the evaluation results of GJO-JOS are competitive to GJO with seven single opposition-based learning strategies (OBLs). We also compare GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produces promising results among seven single OBLs, eight natured-inspired algorithms, and GJO. The experimental results confirm that GJO-JOS generates equilibrium in the balance mechanism effectively.

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Section: ) Statistical Analysis Of Gjo-jos With Seven Variant Algorit...mentioning

confidence: 86%

Section: Table 8 Convergence Curve Of Gjo-jos Vs Six Variant Oppositi...mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

2022

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“…34 This mechanism is a mechanism to increase the exploration ability of the metaheuristics. 35 This mechanism generates a complement value for the candidate to provide pairs of initial populations. In the new population, the pairs compete and the better one is selected as the new candidate which is analyzed by performing the two pairs to the objective function.…”

Section: Developed Coronavirus Herd Immunity Algorithmmentioning

confidence: 99%

Optimized hierarchical radial basis function neural networks by developing coronavirus herd immunity optimizer for solid oxide fuel cells

Zou

Chen

Razmjooy

2023

Concurrency and Computation

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SummaryA new blackbox technique has been presented in this article for model estimation of solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a hierarchical radial basis function (HRBF). The presented method is then developed by a new modified metaheuristic called developed coronavirus herd immunity algorithm (DCHIA). The suggested model has been named DCHIA‐HRBF. The proposed model is then trained by some data and prepared for identification and prediction. The model is then analyzed and put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. The results show that the best cost for the performance index which is the network error, is achieved by the proposed developed coronavirus herd immunity algorithm with about 119.442, which is satisfying for the considered function and target against the other state‐of‐the‐art methods. As a result, the simulation results specified that the suggested DCHIA‐HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs.

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“…Bukar et al. [26] created a rule‐based energy management approach to program the long‐standing ideal capability of a standalone microgrid that is improved and optimized using an algorithm under the title of multiobjective propeller. This research suggests a REMS (a Rule‐based Energy Management Strategy) optimized based on a GHA (Grasshopper Algorithm) that takes inspiration from nature.…”

Section: Introductionmentioning

confidence: 99%

A new optimal energy management strategy of microgrids using chaotic map‐based chameleon swarm algorithm

Ren,

Hou,

Jia

et al. 2023

IET Renewable Power Gen

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This study provides an optimal and efficient energy management strategy (EMS) for the cost-effective performance of a combined solar and green energy microgrid in both independent and grid-connected modes. A microgrid is formed by the system that includes a fuel cell (FC), a battery storage (BS), and a photovoltaic system (PV). Evidently, the unguaranteed features of the renewable energy and load electricity generate instability problems as well as economic ones, like operational expenses. To tackle these issues, a novel procedure is proposed that has been improved by a modified metaheuristic algorithm, called chaotic map-based chameleon Swarm Algorithm (CCSA). In this method, the simulation is based on a one-day planning perspective. The method aims to supply the power requirements of the load at the lowest possible cost under a constant DC bus voltage, protect the battery from overcharging and depletion, and improve the efficiency of the total system. To illustrate the suggested method's effectiveness, the simulation results of CCSA are compared with some studied methods in the literature, including GAMS, bald eagle search optimization algorithm (BEOA), original chameleon swarm algorithm (CSA), and grey wolf optimization algorithm (GWOA).

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Model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal Optimization

Cited by 36 publications

References 28 publications

Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

Optimized hierarchical radial basis function neural networks by developing coronavirus herd immunity optimizer for solid oxide fuel cells

A new optimal energy management strategy of microgrids using chaotic map‐based chameleon swarm algorithm

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