Optimum discrete balancing of the threshing drum using Jaya algorithm

Singh, Prem; Chaudhary, Himanshu

doi:10.1080/15397734.2019.1701489

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Singh, Prem, and Himanshu Chaudhary [87] introduced a discreate version of JAYA algorithm for optimum twoplane balancing of the threshing drum. In their algorithm, the continuous solutions are converted to discrete solutions using discrete constraints of predefined values of design variables.…”

Section: Discrete Jaya Algorithmmentioning

confidence: 99%

“…The viability and efficiency of JAYA-based algorithms make it successfully applied in solving different real-world applications. These applications include mathematical functions [88,101], feature selection [59,82], image processing [130,131], energy [99,132], design proportional-integral-derivative (PID) controller [133,134], economic dispatch [135,136], communication [72,137], electrical and power system [137,138], fracture mechanics [139,140], environmental engineering [96,141] , manufacturing industries [120,121], structural design [111,142], truss structures [57,85], planing and scheduling [50,87], and others [105,143].…”

Section: Applications Of Jaya Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

112

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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Section: Discrete Jaya Algorithmmentioning

confidence: 99%

Section: Applications Of Jaya Algorithmsmentioning

confidence: 99%

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

112

View full text Add to dashboard Cite

show abstract

Research on Performance Evaluation Method of Rice Thresher Based on Neural Network

Zhang

et al. 2022

Actuators

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Because the threshing device of a combine harvester determines the harvesting level and threshing separation performance of a combine harvester, the analysis and study of the threshing device of a combine harvester is key to improving its performance. Based on the threshing device of a half-feed combine harvester, the simulation model of a discrete element threshing device is established in this paper. With the threshing drum rotation speed, feed volume, and concave sieve vibration frequency as the variable factors, the BP neural network model and linear regression equation model established for the loss rate and impurity content for two kinds of threshing performance indicators, respectively, and through the discrete element threshing performance test, two kinds of methods of threshing performance prediction are analyzed. The results show that the neural network and linear regression can be used for the threshing performance indicators, however, the BP neural network prediction effect has a better prediction precision, better reliability, and the trained neural network can be used in the general case of the threshing performance indicators. This provides a new idea for improving the threshing performance of a combine harvester.

show abstract

Optimum discrete balancing of the threshing drum using Jaya algorithm

Cited by 2 publications

References 32 publications

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Research on Performance Evaluation Method of Rice Thresher Based on Neural Network

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