Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection
Abstract The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity in real‐world problems. Specifically, it shows deficiencies in terms of limited local exploration capabilities and less precise solutions. T...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
|
| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850156419633905664 |
|---|---|
| author | Jinge Shi Yi Chen Ali Asghar Heidari Zhennao Cai Huiling Chen Guoxi Liang |
| author_facet | Jinge Shi Yi Chen Ali Asghar Heidari Zhennao Cai Huiling Chen Guoxi Liang |
| author_sort | Jinge Shi |
| collection | DOAJ |
| description | Abstract The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity in real‐world problems. Specifically, it shows deficiencies in terms of limited local exploration capabilities and less precise solutions. Therefore, this research aims to integrate the topological search (TS) mechanism with the gradient search rule (GSR) into the framework of RUN, introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm. The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution, enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration. To prevent the algorithm from becoming trapped in local optima, the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space. This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN. Additionally, the evaluation includes real‐world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm. The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN. |
| format | Article |
| id | doaj-art-3f4f8e6a8a9f4f27bd39a133ba6aac5f |
| institution | OA Journals |
| issn | 2468-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-3f4f8e6a8a9f4f27bd39a133ba6aac5f2025-08-20T02:24:33ZengWileyCAAI Transactions on Intelligence Technology2468-23222025-04-0110255761410.1049/cit2.12387Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selectionJinge Shi0Yi Chen1Ali Asghar Heidari2Zhennao Cai3Huiling Chen4Guoxi Liang5Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou ChinaDepartment of Computer Science and Artificial Intelligence Wenzhou University Wenzhou ChinaSchool of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran IranDepartment of Computer Science and Artificial Intelligence Wenzhou University Wenzhou ChinaDepartment of Computer Science and Artificial Intelligence Wenzhou University Wenzhou ChinaDepartment of Artificial Intelligence Wenzhou Polytechnic Wenzhou ChinaAbstract The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity in real‐world problems. Specifically, it shows deficiencies in terms of limited local exploration capabilities and less precise solutions. Therefore, this research aims to integrate the topological search (TS) mechanism with the gradient search rule (GSR) into the framework of RUN, introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm. The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution, enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration. To prevent the algorithm from becoming trapped in local optima, the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space. This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN. Additionally, the evaluation includes real‐world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm. The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.https://doi.org/10.1049/cit2.12387engineering designgradient search rulemetaheuristic algorithmRunge–Kutta optimizertopological search |
| spellingShingle | Jinge Shi Yi Chen Ali Asghar Heidari Zhennao Cai Huiling Chen Guoxi Liang Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection CAAI Transactions on Intelligence Technology engineering design gradient search rule metaheuristic algorithm Runge–Kutta optimizer topological search |
| title | Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection |
| title_full | Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection |
| title_fullStr | Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection |
| title_full_unstemmed | Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection |
| title_short | Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection |
| title_sort | topological search and gradient descent boosted runge kutta optimiser with application to engineering design and feature selection |
| topic | engineering design gradient search rule metaheuristic algorithm Runge–Kutta optimizer topological search |
| url | https://doi.org/10.1049/cit2.12387 |
| work_keys_str_mv | AT jingeshi topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection AT yichen topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection AT aliasgharheidari topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection AT zhennaocai topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection AT huilingchen topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection AT guoxiliang topologicalsearchandgradientdescentboostedrungekuttaoptimiserwithapplicationtoengineeringdesignandfeatureselection |