An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project
Abstract In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while in...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87350-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850063956868071424 |
|---|---|
| author | Jinyan Shao Yuan Lu Yi Sun Lei Zhao |
| author_facet | Jinyan Shao Yuan Lu Yi Sun Lei Zhao |
| author_sort | Jinyan Shao |
| collection | DOAJ |
| description | Abstract In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while innovatively combining the fast non-dominated sorting technique to effectively evaluate and approximate the Pareto optimal solution set. To enhance the diversity and generalization of the solution set, the crowding distance mechanism is introduced, ensuring a good balance between multiple optimization objectives and a wider coverage of the solution space. Additionally, an acceleration factor based on trigonometric functions and an adaptive Gaussian mutation strategy are incorporated, improving the exploration ability of the particles in the search space and facilitating their movement towards the global optimal solution more effectively. The performance of the algorithm is verified using the multi-modal multi-objective benchmark function set provided by CEC2020, and comparisons are made with five advanced multi-objective metaheuristics. The MOIPSO algorithm is also applied to solve the design problem of rail transit upper cover foundation pit, further demonstrating the practical effectiveness of the proposed algorithm. The results show that MOIPSO not only performs well in multi-objective function testing but also proves highly competitive in solving real-world engineering problems. Note that the source codes of MOGWO are publicly available at https://au.mathworks.com/matlabcentral/fileexchange/177404-moipso-optimization-engineering-problem . |
| format | Article |
| id | doaj-art-e21f4edcb0e94b389c9147a899b6720e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e21f4edcb0e94b389c9147a899b6720e2025-08-20T02:49:26ZengNature PortfolioScientific Reports2045-23222025-03-0115112510.1038/s41598-025-87350-8An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover projectJinyan Shao0Yuan Lu1Yi Sun2Lei Zhao3TOD Institute, Beijing Jiaotong UniversitySchool of Architecture and Design, Beijing Jiao Tong UniversityChina Architecture Design and Research Institute Co., Ltd. Shanghai BranchBeijing Urban Construction Design and Development Group Co., LtdAbstract In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while innovatively combining the fast non-dominated sorting technique to effectively evaluate and approximate the Pareto optimal solution set. To enhance the diversity and generalization of the solution set, the crowding distance mechanism is introduced, ensuring a good balance between multiple optimization objectives and a wider coverage of the solution space. Additionally, an acceleration factor based on trigonometric functions and an adaptive Gaussian mutation strategy are incorporated, improving the exploration ability of the particles in the search space and facilitating their movement towards the global optimal solution more effectively. The performance of the algorithm is verified using the multi-modal multi-objective benchmark function set provided by CEC2020, and comparisons are made with five advanced multi-objective metaheuristics. The MOIPSO algorithm is also applied to solve the design problem of rail transit upper cover foundation pit, further demonstrating the practical effectiveness of the proposed algorithm. The results show that MOIPSO not only performs well in multi-objective function testing but also proves highly competitive in solving real-world engineering problems. Note that the source codes of MOGWO are publicly available at https://au.mathworks.com/matlabcentral/fileexchange/177404-moipso-optimization-engineering-problem .https://doi.org/10.1038/s41598-025-87350-8Multi-objective optimization problemsParticle swarm optimizationMulti-objective particle swarm optimization (MOIPSO)Rail transit upper cover foundation pit |
| spellingShingle | Jinyan Shao Yuan Lu Yi Sun Lei Zhao An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project Scientific Reports Multi-objective optimization problems Particle swarm optimization Multi-objective particle swarm optimization (MOIPSO) Rail transit upper cover foundation pit |
| title | An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| title_full | An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| title_fullStr | An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| title_full_unstemmed | An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| title_short | An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| title_sort | improved multi objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project |
| topic | Multi-objective optimization problems Particle swarm optimization Multi-objective particle swarm optimization (MOIPSO) Rail transit upper cover foundation pit |
| url | https://doi.org/10.1038/s41598-025-87350-8 |
| work_keys_str_mv | AT jinyanshao animprovedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT yuanlu animprovedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT yisun animprovedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT leizhao animprovedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT jinyanshao improvedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT yuanlu improvedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT yisun improvedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject AT leizhao improvedmultiobjectiveparticleswarmoptimizationalgorithmforthedesignoffoundationpitofrailtransituppercoverproject |