<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first investigates the implementation process of the particle swarm algorithm by introducing the weight factor <italic>ω</italic> to make the algorithm population iterative, using the particles to perform a local search and make them stay near the optimal solution of the algorithm to find the best. Then, we discuss the improvement of the particle swarm optimization algorithm by adding inertia weight <italic>ω</italic> and shrinkage factor to improve the performance of the algorithm and analyze the convergence of the optimization algorithm to determine the convergence conditions and the state of the system. Finally, the improved particle swarm algorithm was used to evaluate the development status of vocational education, analyze its problems, and propose countermeasures for high-quality development. In terms of problems, 68% believe that there is a misalignment in management. 69% believe that the internal management system of the school is not perfect. 66% believe that the school does not have enough funds for the cultivation of talents. As for the learning atmosphere, 294 people think the learning atmosphere is weak, and 487 people think the learning atmosphere is average. The study of this paper is important for the development and optimization of vocational education.</p>
</abstract>

This paper first investigates the implementation process of the particle swarm algorithm by introducing the weight factor ω to make the algorithm population iterative, using the particles to perform a local search and make them stay near the optimal solution of the algorithm to find the best. Then, we discuss the improvement of the particle swarm optimization algorithm by adding inertia weight ω and shrinkage factor to improve the performance of the algorithm and analyze the convergence of the optimization algorithm to determine the convergence conditions and the state of the system. Finally, the improved particle swarm algorithm was used to evaluate the development status of vocational education, analyze its problems, and propose countermeasures for high-quality development. In terms of problems, 68% believe that there is a misalignment in management. 69% believe that the internal management system of the school is not perfect. 66% believe that the school does not have enough funds for the cultivation of talents. As for the learning atmosphere, 294 people think the learning atmosphere is weak, and 487 people think the learning atmosphere is average. The study of this paper is important for the development and optimization of vocational education.

Research on high quality development system of vocational education based on improved particle swarm optimization algorithm

Technological University of the Philippines

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first investigates the implementation process of the particle swarm algorithm by introducing the weight factor ω to make the algorithm population...

Research on high quality development system of vocational education based on improved particle swarm optimization algorithm

Online veröffentlicht: 02. Okt. 2023

Seitenbereich: -

Eingereicht: 18. Okt. 2022

Akzeptiert: 12. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00442

Schlüsselwörter
Improved particle swarm optimization algorithm, Vocational education, Shrinkage factor, Shrinkage factor, Inertia weights

© 2023 Qiang Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on high quality development system of vocational education based on improved particle swarm optimization algorithm

Online veröffentlicht: 02. Okt. 2023

Seitenbereich: -

Eingereicht: 18. Okt. 2022

Akzeptiert: 12. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00442

SchlüsselwörterImproved particle swarm optimization algorithm, Vocational education, Shrinkage factor, Shrinkage factor, Inertia weights

© 2023 Qiang Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Improved particle swarm optimization algorithm, Vocational education, Shrinkage factor, Shrinkage factor, Inertia weights