Improved marine predators algorithm for engineering design optimization problems
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 industr...
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| Published in: | Scientific reports Vol. 14; no. 1; pp. 13000 - 23 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
06.06.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 13000 |
| Author | Hua, Xu Chun, Ye Qi, Chen Yao, Ye Xin |
| Author_xml | – sequence: 1 givenname: Ye surname: Chun fullname: Chun, Ye email: yechun1979@sohu.com organization: Internet of Things Engineering College, Jiangsu Vocational College of Information Technology, School of Artificial Intelligence and Computer Science, Jiangnan University – sequence: 2 givenname: Xu surname: Hua fullname: Hua, Xu organization: School of Artificial Intelligence and Computer Science, Jiangnan University – sequence: 3 givenname: Chen surname: Qi fullname: Qi, Chen organization: Institute of Civil Engineering, Jiangsu Vocational College of Information Technology – sequence: 4 givenname: Ye Xin surname: Yao fullname: Yao, Ye Xin organization: Wuxi Furen High School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38844819$$D View this record in MEDLINE/PubMed |
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| Keywords | Social strategy Improved marine predators algorithm Self-adaptive weight Complex industrial engineering design problems |
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| Snippet | The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages... Abstract The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more... |
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| SubjectTerms | 639/166 639/705 Algorithms Complex industrial engineering design problems Design engineering Design optimization Humanities and Social Sciences Improved marine predators algorithm Industrial engineering Marine ecosystems multidisciplinary Objective function Predators Science Science (multidisciplinary) Self-adaptive weight Social discrimination learning Social strategy |
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| Title | Improved marine predators algorithm for engineering design optimization problems |
| URI | https://link.springer.com/article/10.1038/s41598-024-63826-x https://www.ncbi.nlm.nih.gov/pubmed/38844819 https://www.proquest.com/docview/3065127944 https://www.proquest.com/docview/3065985841 https://pubmed.ncbi.nlm.nih.gov/PMC11637075 https://doaj.org/article/81fe44c3b0664e639602040691fe73c4 |
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