Powerful enhanced Jaya algorithm for efficiently optimizing numerical and engineering problems

Gholami, Jafar; Kamankesh, Mohamad Reza; Mohammadi, Seyed Abolghasem; Hosseinkhani, Elahe; Abdi, Somayeh

doi:10.1007/s00500-022-06909-z

Cited by 6 publications

(2 citation statements)

References 71 publications

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“…It includes energy, transportation, electric power, aviation, agriculture, environment and other fields closely related to national economy, social development and defense industry. The research and application fields of this major are very wide [7][8].…”

Section: Major In Power Mechanical Engineeringmentioning

confidence: 99%

Employ Ability Evaluation Based on Power Mechanical Engineering

2020

KME

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With the continuous development of society, mechanical design and manufacturing technology is also making rapid progress. Power mechanical engineering major is a demand for more professional. Employ ability refers to the basic qualities of students. In order to enhance students' employ ability, this paper explores the main principles of their ability evaluation. It can be regarded as one of the most important, energetic and potential levels for a person's survival and development. Because of the complexity of technology, the employment prospects of mechanical engineering students depend on their innovation and practical ability. This paper mainly uses the survey method and analytic hierarchy process to study the employment of power mechanical engineering major. According to the survey results, the employment ability of the students in this major is above the average level, but only 5.8 percent of them have excellent knowledge ability. This falls far short of the country's needs. Therefore, it is necessary to cultivate graduates' knowledge and practical skills from these two aspects.

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Section: Major In Power Mechanical Engineeringmentioning

confidence: 99%

Employ Ability Evaluation Based on Power Mechanical Engineering

2020

KME

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“…Ghandourah et al (2022) introduce an improved Artificial Neural Network (ANN) designed for predicting the displacement of composite material pipelines subjected to varying falling hammer impact velocities. Ouladbrahim et al (2022) employed various optimization techniques (WOA-ANN, GA-ANN, AOA-ANN, WOABAT-ANN) for crack length identification, while Gholami, Kamankesh, Mohammadi, Hosseinkhani, and Abdi (2022) enhanced the Jaya algorithm's performance, resulting in the Powerful Enhanced Jaya (PEJAYA), which outperformed other algorithms in various applications. Khatir, Capozucca, Khatir, and Magagnini (2022) integrated ANN with the Butterfly Optimization Algorithm (BOA) to predict cracks in steel and aluminum beams.…”

Section: Introductionmentioning

confidence: 99%

Numerical study of the influence of layers number and their orientation in a CFRP using improved ANN based on Enhanced Jaya

Oulad Brahim

2024

HCMCOUJS - Advances in Computational Structures

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The advancement of technology in the field of artificial intelligence has facilitated significant progress in various areas, particularly in optimizing the structural configuration of multilayer composites. This study's primary objective is to investigate how geometric parameters, such as fiber orientation and the number of layers influence the mechanical properties of these materials. To predict fracture toughness in bending tests, we employed a hybrid optimization technique (E-Jaya-ANN) and compared it with the Jaya-ANN method to assess the improved approach's accuracy. Furthermore, we developed a numerical model based on the Hashin damage criterion using ABAQUS software to anticipate the response of composite specimens (CFRP) when subjected to a bending load. We validated the reliability of the code results by comparing them with experimental data. Subsequently, we generated a series of numerical results, representing various practical scenarios, to serve as the foundation for training an artificial neural network (ANN). This enabled us to obtain an enhanced architecture for the laminate layers.

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