Improving the convergence velocity of the balancing composite motion optimisation algorithm
The balancing composite motion optimisation (BCMO), is a metaheuristic algorithm. However, the strengths of BCMO lie in its ability to extract information from the current generation and balance local and global searches. As a result, the convergence velocity of BCMO is superior to other optimisati...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vietnam Ministry of Science and Technology
2024-12-01
|
| Series: | Vietnam Journal of Science, Technology and Engineering |
| Subjects: | |
| Online Access: | https://vietnamscience.vjst.vn/index.php/vjste/article/view/1233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841558843416379392 |
|---|---|
| author | Nang Duc Bui Thang Quan Nguyen Hieu Chi Phan Dung-Nhan Nguyen Bui |
| author_facet | Nang Duc Bui Thang Quan Nguyen Hieu Chi Phan Dung-Nhan Nguyen Bui |
| author_sort | Nang Duc Bui |
| collection | DOAJ |
| description |
The balancing composite motion optimisation (BCMO), is a metaheuristic algorithm. However, the strengths of BCMO lie in its ability to extract information from the current generation and balance local and global searches. As a result, the convergence velocity of BCMO is superior to other optimisation methods, with a parameter-free and simplified computational procedure. This study proposes an enhanced algorithm, BCMO_A, designed to further increase the convergence velocity of the original BCMO. The proposed enhancement involves introducing an individual selection operator for the new generation. Three candidate options for the selection process were explored, showing minor differences. The best individuals from the two available populations were selected before interaction. The study also emphasises the importance of the terminal condition, which is based on the best value and the mean of objective functions in the current population. Numerical experiments were conducted on 23 classical benchmark functions, and the results were analysed using official statistical tests. The findings demonstrate that the original BCMO has been significantly improved, achieving better convergence speed, with optimised values reached earlier.
|
| format | Article |
| id | doaj-art-71f59a5faa8846aeb8be66e1eb4b2f45 |
| institution | Kabale University |
| issn | 2525-2461 2615-9937 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Vietnam Ministry of Science and Technology |
| record_format | Article |
| series | Vietnam Journal of Science, Technology and Engineering |
| spelling | doaj-art-71f59a5faa8846aeb8be66e1eb4b2f452025-01-06T02:53:59ZengVietnam Ministry of Science and TechnologyVietnam Journal of Science, Technology and Engineering2525-24612615-99372024-12-0166410.31276/VJSTE.2024.0029Improving the convergence velocity of the balancing composite motion optimisation algorithmNang Duc BuiThang Quan NguyenHieu Chi PhanDung-Nhan Nguyen Bui The balancing composite motion optimisation (BCMO), is a metaheuristic algorithm. However, the strengths of BCMO lie in its ability to extract information from the current generation and balance local and global searches. As a result, the convergence velocity of BCMO is superior to other optimisation methods, with a parameter-free and simplified computational procedure. This study proposes an enhanced algorithm, BCMO_A, designed to further increase the convergence velocity of the original BCMO. The proposed enhancement involves introducing an individual selection operator for the new generation. Three candidate options for the selection process were explored, showing minor differences. The best individuals from the two available populations were selected before interaction. The study also emphasises the importance of the terminal condition, which is based on the best value and the mean of objective functions in the current population. Numerical experiments were conducted on 23 classical benchmark functions, and the results were analysed using official statistical tests. The findings demonstrate that the original BCMO has been significantly improved, achieving better convergence speed, with optimised values reached earlier. https://vietnamscience.vjst.vn/index.php/vjste/article/view/1233algorithmsbalancing composite motion optimisationconvergence velocitymetaheuristicoptimisation |
| spellingShingle | Nang Duc Bui Thang Quan Nguyen Hieu Chi Phan Dung-Nhan Nguyen Bui Improving the convergence velocity of the balancing composite motion optimisation algorithm Vietnam Journal of Science, Technology and Engineering algorithms balancing composite motion optimisation convergence velocity metaheuristic optimisation |
| title | Improving the convergence velocity of the balancing composite motion optimisation algorithm |
| title_full | Improving the convergence velocity of the balancing composite motion optimisation algorithm |
| title_fullStr | Improving the convergence velocity of the balancing composite motion optimisation algorithm |
| title_full_unstemmed | Improving the convergence velocity of the balancing composite motion optimisation algorithm |
| title_short | Improving the convergence velocity of the balancing composite motion optimisation algorithm |
| title_sort | improving the convergence velocity of the balancing composite motion optimisation algorithm |
| topic | algorithms balancing composite motion optimisation convergence velocity metaheuristic optimisation |
| url | https://vietnamscience.vjst.vn/index.php/vjste/article/view/1233 |
| work_keys_str_mv | AT nangducbui improvingtheconvergencevelocityofthebalancingcompositemotionoptimisationalgorithm AT thangquannguyen improvingtheconvergencevelocityofthebalancingcompositemotionoptimisationalgorithm AT hieuchiphan improvingtheconvergencevelocityofthebalancingcompositemotionoptimisationalgorithm AT dungnhannguyenbui improvingtheconvergencevelocityofthebalancingcompositemotionoptimisationalgorithm |