Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation
Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been intro...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/407 |
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| author | Tianshan Dong Shenyan Chen Hai Huang Chao Han Ziqi Dai Zihan Yang |
| author_facet | Tianshan Dong Shenyan Chen Hai Huang Chao Han Ziqi Dai Zihan Yang |
| author_sort | Tianshan Dong |
| collection | DOAJ |
| description | Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique. |
| first_indexed | 2024-03-10T03:49:42Z |
| format | Article |
| id | doaj.art-d616def828494aafaf28a0d0becc2089 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:49:42Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d616def828494aafaf28a0d0becc20892023-11-23T11:12:33ZengMDPI AGApplied Sciences2076-34172021-12-0112140710.3390/app12010407Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint ApproximationTianshan Dong0Shenyan Chen1Hai Huang2Chao Han3Ziqi Dai4Zihan Yang5School of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaTruss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.https://www.mdpi.com/2076-3417/12/1/407topology optimizationlarge-scale trusslabel–clip–splice techniquea branched multipoint approximationgenetic algorithm |
| spellingShingle | Tianshan Dong Shenyan Chen Hai Huang Chao Han Ziqi Dai Zihan Yang Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation Applied Sciences topology optimization large-scale truss label–clip–splice technique a branched multipoint approximation genetic algorithm |
| title | Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation |
| title_full | Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation |
| title_fullStr | Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation |
| title_full_unstemmed | Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation |
| title_short | Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation |
| title_sort | large scale truss topology and sizing optimization by an improved genetic algorithm with multipoint approximation |
| topic | topology optimization large-scale truss label–clip–splice technique a branched multipoint approximation genetic algorithm |
| url | https://www.mdpi.com/2076-3417/12/1/407 |
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