Improved marine predators algorithm for engineering design optimization problems
Abstract The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practica...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63826-x |
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| _version_ | 1832594701501333504 |
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| author | Ye Chun Xu Hua Chen Qi Ye Xin Yao |
| author_facet | Ye Chun Xu Hua Chen Qi Ye Xin Yao |
| author_sort | Ye Chun |
| collection | DOAJ |
| description | Abstract The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems. |
| format | Article |
| id | doaj-art-81fe44c3b0664e639602040691fe73c4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-81fe44c3b0664e639602040691fe73c42025-01-19T12:24:54ZengNature PortfolioScientific Reports2045-23222024-06-0114112310.1038/s41598-024-63826-xImproved marine predators algorithm for engineering design optimization problemsYe Chun0Xu Hua1Chen Qi2Ye Xin Yao3Internet of Things Engineering College, Jiangsu Vocational College of Information TechnologySchool of Artificial Intelligence and Computer Science, Jiangnan UniversityInstitute of Civil Engineering, Jiangsu Vocational College of Information TechnologyWuxi Furen High SchoolAbstract The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.https://doi.org/10.1038/s41598-024-63826-xImproved marine predators algorithmSelf-adaptive weightSocial strategyComplex industrial engineering design problems |
| spellingShingle | Ye Chun Xu Hua Chen Qi Ye Xin Yao Improved marine predators algorithm for engineering design optimization problems Scientific Reports Improved marine predators algorithm Self-adaptive weight Social strategy Complex industrial engineering design problems |
| title | Improved marine predators algorithm for engineering design optimization problems |
| title_full | Improved marine predators algorithm for engineering design optimization problems |
| title_fullStr | Improved marine predators algorithm for engineering design optimization problems |
| title_full_unstemmed | Improved marine predators algorithm for engineering design optimization problems |
| title_short | Improved marine predators algorithm for engineering design optimization problems |
| title_sort | improved marine predators algorithm for engineering design optimization problems |
| topic | Improved marine predators algorithm Self-adaptive weight Social strategy Complex industrial engineering design problems |
| url | https://doi.org/10.1038/s41598-024-63826-x |
| work_keys_str_mv | AT yechun improvedmarinepredatorsalgorithmforengineeringdesignoptimizationproblems AT xuhua improvedmarinepredatorsalgorithmforengineeringdesignoptimizationproblems AT chenqi improvedmarinepredatorsalgorithmforengineeringdesignoptimizationproblems AT yexinyao improvedmarinepredatorsalgorithmforengineeringdesignoptimizationproblems |