This study proposes the goose algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other indiv...
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This study proposes the goose algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The goose algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the goose algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problems, Pressure vessel Design Problems, and the Pathological IgG Fraction in the Nervous System, four renowned real-world challenges. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.
In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., xi 1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepa...
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In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., xi 1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{1}$$\end{document}, xi 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{2}$$\end{document}, xi 3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{3}$$\end{document}, xi 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{4}$$\end{document}, RC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{\text{C}}$$\end{document}, lambda\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}, and b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b$$\end{document}. The fuel cells (FCs) involve multiple
Amidst the robust advancement of new energy generation technologies, wind power and photovoltaic (PV) power are progressively claiming a larger share within the power grid. Concurrently, these emerging energy sources ...
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ISBN:
(纸本)9798350349047;9798350349030
Amidst the robust advancement of new energy generation technologies, wind power and photovoltaic (PV) power are progressively claiming a larger share within the power grid. Concurrently, these emerging energy sources are actively engaging in grid frequency regulation. To tackle the issue of dynamic power allocation among various types of frequency regulation sources during power fluctuations, this study devises a multi-source optimal coordinated control model. It takes into account the participation of wind, solar, and energy storage systems. Addressing this challenge, the study employs the goose algorithm from artificial intelligence for problem-solving. This algorithm efficiently facilitates the participation of different types of regulation sources in power distribution within their respective regulation ranges, thereby bolstering the dynamic response regulation of the regional power grid and enhancing system stability. Finally, the study validates the constructed model using an extended IEEE standard 2-area model and assesses the algorithm's performance by juxtaposing it with traditional engineering allocation methods and intelligent optimization algorithms.
Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing predi...
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Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing prediction models typically exhibit two primary shortcomings: on the one hand, the accuracy of life prediction reaches the desired level of precision while failing to achieve a sufficiently fast prediction speed, and on the other hand, generalization is not guaranteed while requiring the model to be robust. These two aspects present a significant challenge to the field of machine learning. In light of the aforementioned issues, we propose a prediction model based on the goose algorithm. Initially, we set the goose algorithm using adaptive initialization of the goose population to guarantee that the goose population is set at the appropriate interval, and we incorporate it into the extreme learning machine model through the improved goose algorithm. goose algorithm is used to predict the service life. Finally, we utilize different types of lithium batteries with varying operational conditions to conduct pertinent case studies to validate the proposed prediction model. The results demonstrated that the average accuracy was above 98% in all validated datasets. The shortest computation time was 0.19 s.
High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prev...
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High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (goose) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions.
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