[1]赵艳敏,霍达..基于遗传模拟退火算法的钢桁架结构优化设计[J].郑州大学学报(工学版),2011,32(06):54-57.
　ZHAO Yan-min,HUO Da.Optimal Design of Steel Truss Structure Based on Genetic Simulated Annealing Algorithm[J].Journal of Zhengzhou University (Engineering Science),2011,32(06):54-57.
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基于遗传模拟退火算法的钢桁架结构优化设计()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
32
期数:
2011年06期
页码:
54-57
栏目:
出版日期:
2011-11-10

文章信息/Info

Title:
Optimal Design of Steel Truss Structure Based on Genetic Simulated Annealing Algorithm
作者:
赵艳敏; 霍达.
北京京北职业技术学院,北京,101400, 北京工业大学建筑工程学院,北京,100022
Author(s):
ZHAO Yan-min1; HUO Da2
1.Northerm Beijing Vocational Education Institute，Beijing 101400，China; 2.Faculty of Architectural Engineering，Beijing U-niversity of ’Technology ，Beijing 100022，China
关键词:
遗传算法;  模拟退火算法;  优化设计
Keywords:
genetic algorithm ;  simulated annealing algorithm ;  optimal design
分类号:
TU323.4
文献标志码:
A
摘要:
将遗传算法(GA)的全局寻优性能好和模拟退火算法(SA)的局部搜索能力强的优点相结合,提出了用于钢桁架结构离散变量优化设计的遗传模拟退火算法(SAGA).以十杆桁架为例对此算法进行了数值实验,并将实验结果与其他优化方法相比较.算例结果表明,遗传模拟退火算法的寻优概率是100％,平均进化代数为35代,其稳定性和求解效率均高于改进的遗传算法.实验结果显示,遗传模拟退火算法在整体搜索同时,采用退火操作进行局部搜索,提高了算法的局部搜索能力,有效克服了遗传算法迭代缓慢的缺点,把遗传模拟退火算法用于钢桁架离散变量的优化设计中是行之有效的.
Abstract:
To combine Cenetic Algorithm ( CA) with Simulated Annealing Algorithm (SA) that the CeneticSimulated Annealing Algorithm (SAGA) was proposed . It had the global searching ability of CA together withthe local fast converging ability of SA. It was applied to the steel truss structural optimization with discrete var-iables and this paper provided the comparison between SACA experiments and other optimal results. The ex-periments showed that the searching optimization probability of SACA was 100% and the average evolved gen-erations is 35 ,which indicated that SACA was more stable and had better seeking efficiency than improvedGA. The SACA improved the local searching ability and overcame evolution slowness defect of CA through ap-plying local searching annealing method.The SACA is an effective method to seek the optimal design of steeltrusses with discrete variables.

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更新日期/Last Update: 1900-01-01