Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems
This paper aims to represent a multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA) to solve real-world constraint engineering problems. The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coef...
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| Veröffentlicht in: | Structural and multidisciplinary optimization 2023-05, Vol.66 (5), p.114, Article 114 |
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| creator | Luo, Qifang Yin, Shihong Zhou, Guo Meng, Weiping Zhao, Yixin Zhou, Yongquan |
| description | This paper aims to represent a multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA) to solve real-world constraint engineering problems. The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coefficients are used to adjust exploration and exploitation trends. The elite archiving mechanism is used to promote the convergence of the algorithm. The crowding distance method is used to maintain the distribution of the Pareto front. The equilibrium pool strategy is used to simulate the cooperative foraging behavior of the slime mould, which helps to enhance the exploration ability of the algorithm. The performance of MOEOSMA is evaluated on the latest CEC2020 functions, eight real-world multi-objective constraint engineering problems, and four large-scale truss structure optimization problems. The experimental results show that the proposed MOEOSMA not only finds more Pareto optimal solutions, but also maintains a good distribution in the decision space and objective space. Statistical results show that MOEOSMA has a strong competitive advantage in terms of convergence, diversity, uniformity, and extensiveness, and its comprehensive performance is significantly better than other comparable algorithms. |
| doi_str_mv | 10.1007/s00158-023-03568-y |
| format | Article |
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The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coefficients are used to adjust exploration and exploitation trends. The elite archiving mechanism is used to promote the convergence of the algorithm. The crowding distance method is used to maintain the distribution of the Pareto front. The equilibrium pool strategy is used to simulate the cooperative foraging behavior of the slime mould, which helps to enhance the exploration ability of the algorithm. The performance of MOEOSMA is evaluated on the latest CEC2020 functions, eight real-world multi-objective constraint engineering problems, and four large-scale truss structure optimization problems. The experimental results show that the proposed MOEOSMA not only finds more Pareto optimal solutions, but also maintains a good distribution in the decision space and objective space. Statistical results show that MOEOSMA has a strong competitive advantage in terms of convergence, diversity, uniformity, and extensiveness, and its comprehensive performance is significantly better than other comparable algorithms.</description><identifier>ISSN: 1615-147X</identifier><identifier>EISSN: 1615-1488</identifier><identifier>DOI: 10.1007/s00158-023-03568-y</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Computational Mathematics and Numerical Analysis ; Convergence ; Engineering ; Engineering Design ; Equilibrium ; Multiple objective analysis ; Optimization ; Pareto optimum ; Performance evaluation ; Research Paper ; Slime ; Theoretical and Applied Mechanics ; Trussed structures</subject><ispartof>Structural and multidisciplinary optimization, 2023-05, Vol.66 (5), p.114, Article 114</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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Yin, Shihong ; Zhou, Guo ; Meng, Weiping ; Zhao, Yixin ; Zhou, Yongquan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-6e52d0b7e6b61e5b5864dbda423f22e11d8d524d6c634e8728a85dfa9a57b3343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Computational Mathematics and Numerical Analysis</topic><topic>Convergence</topic><topic>Engineering</topic><topic>Engineering Design</topic><topic>Equilibrium</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Pareto optimum</topic><topic>Performance evaluation</topic><topic>Research Paper</topic><topic>Slime</topic><topic>Theoretical and Applied Mechanics</topic><topic>Trussed structures</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Qifang</creatorcontrib><creatorcontrib>Yin, Shihong</creatorcontrib><creatorcontrib>Zhou, Guo</creatorcontrib><creatorcontrib>Meng, Weiping</creatorcontrib><creatorcontrib>Zhao, Yixin</creatorcontrib><creatorcontrib>Zhou, Yongquan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Structural and multidisciplinary optimization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Qifang</au><au>Yin, Shihong</au><au>Zhou, Guo</au><au>Meng, Weiping</au><au>Zhao, Yixin</au><au>Zhou, Yongquan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems</atitle><jtitle>Structural and multidisciplinary optimization</jtitle><stitle>Struct Multidisc Optim</stitle><date>2023-05-01</date><risdate>2023</risdate><volume>66</volume><issue>5</issue><spage>114</spage><pages>114-</pages><artnum>114</artnum><issn>1615-147X</issn><eissn>1615-1488</eissn><abstract>This paper aims to represent a multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA) to solve real-world constraint engineering problems. The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coefficients are used to adjust exploration and exploitation trends. The elite archiving mechanism is used to promote the convergence of the algorithm. The crowding distance method is used to maintain the distribution of the Pareto front. The equilibrium pool strategy is used to simulate the cooperative foraging behavior of the slime mould, which helps to enhance the exploration ability of the algorithm. The performance of MOEOSMA is evaluated on the latest CEC2020 functions, eight real-world multi-objective constraint engineering problems, and four large-scale truss structure optimization problems. The experimental results show that the proposed MOEOSMA not only finds more Pareto optimal solutions, but also maintains a good distribution in the decision space and objective space. 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| subjects | Algorithms Computational Mathematics and Numerical Analysis Convergence Engineering Engineering Design Equilibrium Multiple objective analysis Optimization Pareto optimum Performance evaluation Research Paper Slime Theoretical and Applied Mechanics Trussed structures |
| title | Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems |
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