An Improved Strategy for Genetic Evolutionary Structural Optimization
Genetic evolutionary structural optimization (GESO) method is an integration of the genetic algorithm (GA) and evolutionary structural optimization (ESO). It has proven to be more powerful in searching for global optimal response and requires less computational efforts than ESO or GA. However, GESO...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/5924198 |
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| author | Nannan Cui Shiping Huang Xiaoyan Ding |
| author_facet | Nannan Cui Shiping Huang Xiaoyan Ding |
| author_sort | Nannan Cui |
| collection | DOAJ |
| description | Genetic evolutionary structural optimization (GESO) method is an integration of the genetic algorithm (GA) and evolutionary structural optimization (ESO). It has proven to be more powerful in searching for global optimal response and requires less computational efforts than ESO or GA. However, GESO breaks down in the Zhou-Rozvany problem. Furthermore, GESO occasionally misses the optimum layout of a structure in the evolution for its characteristic of probabilistic deletion. This paper proposes an improved strategy that has been realized by MATLAB programming. A penalty gene is introduced into the GESO strategy and the performance index (PI) is monitored during the optimization process. Once the PI is less than the preset value which means that the calculation error of some element’s sensitivity is too big or some important elements are mistakenly removed, the penalty gene becomes active to recover those elements and reduce their selection probability in the next iterations. It should be noted that this improvement strategy is different from “freezing,” and the recovered elements could still be removed, if necessary. The improved GESO performs well in the Zhou-Rozvany problem. In other numerical examples, the results indicate that the improved GESO has inherited the computational efficiency of GESO and more importantly increased the optimizing capacity and stability. |
| format | Article |
| id | doaj-art-13ee05ccd7584eb0b950063c9d6161a2 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-13ee05ccd7584eb0b950063c9d6161a22025-02-03T06:43:36ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/59241985924198An Improved Strategy for Genetic Evolutionary Structural OptimizationNannan Cui0Shiping Huang1Xiaoyan Ding2School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, ChinaShandong Hi-Speed Group Co., Ltd., Jinan 250098, ChinaGenetic evolutionary structural optimization (GESO) method is an integration of the genetic algorithm (GA) and evolutionary structural optimization (ESO). It has proven to be more powerful in searching for global optimal response and requires less computational efforts than ESO or GA. However, GESO breaks down in the Zhou-Rozvany problem. Furthermore, GESO occasionally misses the optimum layout of a structure in the evolution for its characteristic of probabilistic deletion. This paper proposes an improved strategy that has been realized by MATLAB programming. A penalty gene is introduced into the GESO strategy and the performance index (PI) is monitored during the optimization process. Once the PI is less than the preset value which means that the calculation error of some element’s sensitivity is too big or some important elements are mistakenly removed, the penalty gene becomes active to recover those elements and reduce their selection probability in the next iterations. It should be noted that this improvement strategy is different from “freezing,” and the recovered elements could still be removed, if necessary. The improved GESO performs well in the Zhou-Rozvany problem. In other numerical examples, the results indicate that the improved GESO has inherited the computational efficiency of GESO and more importantly increased the optimizing capacity and stability.http://dx.doi.org/10.1155/2020/5924198 |
| spellingShingle | Nannan Cui Shiping Huang Xiaoyan Ding An Improved Strategy for Genetic Evolutionary Structural Optimization Advances in Civil Engineering |
| title | An Improved Strategy for Genetic Evolutionary Structural Optimization |
| title_full | An Improved Strategy for Genetic Evolutionary Structural Optimization |
| title_fullStr | An Improved Strategy for Genetic Evolutionary Structural Optimization |
| title_full_unstemmed | An Improved Strategy for Genetic Evolutionary Structural Optimization |
| title_short | An Improved Strategy for Genetic Evolutionary Structural Optimization |
| title_sort | improved strategy for genetic evolutionary structural optimization |
| url | http://dx.doi.org/10.1155/2020/5924198 |
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