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

<p>In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.</p>
</abstract>

In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.

Design of computer algorithms based on parametric models

Shanxi Technology and Business University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Design of computer algorithms based on parametric models"}

In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on...

Design of computer algorithms based on parametric models

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0575

Parole chiave
Completion time, Load balancing degree, Execution cost, Optimal parameters, Algorithm design

© 2024 Jiexian Bai et al., published by Sciendo

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

Design of computer algorithms based on parametric models

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0575

Parole chiaveCompletion time, Load balancing degree, Execution cost, Optimal parameters, Algorithm design

© 2024 Jiexian Bai et al., published by Sciendo

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

Parole chiave
Completion time, Load balancing degree, Execution cost, Optimal parameters, Algorithm design