基于混合动态罚函数改进协同优化算法的船舶结构静动力学优化设计

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摘要基于标准协同优化算法，针对已有改进协同优化算法的松弛因子法和罚函数法的缺陷，引入松弛因子构造混合动态罚函数改进协同优化算法，在Isight优化软件中采用了同时具备非支配排序遗传算法和自适应模拟退火算法优点的混合算法优化系统级。将改进的协同优化算法应用到船舶结构的多目标优化设计中，对船舶机舱结构的静力学和动力学特性进行优化，得到最优解并与已有的基于动态罚函数的协同优化算法结果进行比较。优化结果表明，基于混合动态罚函数改进协同优化算法的迭代次数更少，目标值更优且学科间不一致信息更小，对于实际船舶工程上的多目标多学科结构优化有一定应用价值。

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	郭天奇1
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	夏益美3
	王福花3
	王德禹1
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关键词 ：协同优化算法, 混合动态罚函数, 混合算法, 船舶机舱

Abstract：Based on the standard collaborative optimization and aimed at the limitation of existing improved collaborative optimization like the relaxation factor method and the penalty function method, the collaborative optimization algorithm was improved by introducing the relaxation factor to construct the mixed dynamic penalty function.A hybrid algorithm based on the Non-dominated Sorting Genetic Algorithm and Adaptive Simulated Annealing was proposed for system-level optimization through the Isight software.The improved collaborative optimization algorithm was applied to the multi-objective optimization design of ship structures to optimize the static and dynamic characteristics of the marine engine room.The optimal solution was obtained and compared with the results of existing collaborative optimization algorithm based on the dynamic penalty function.It was shown that the improved algorithm has fewer iterations, better target value, and smaller interdisciplinary inconsistency information, which has certain value for multi-objective and multi-disciplinary structural optimization in actual ship engineering application.

Key words： collaborative optimization algorithm mixed dynamic penalty function hybrid algorithm marine engine room

收稿日期: 2018-02-27 出版日期: 2019-10-15

引用本文:

郭天奇1,2，夏益美3，王福花3，王德禹1,2?. 基于混合动态罚函数改进协同优化算法的船舶结构静动力学优化设计[J]. 振动与冲击, 2019, 38(20): 70-76.
GUO Tianqi 1,2，XIA Yimei3，WANG Fuhua3，WANG Deyu 1,2. An improved collaborative optimization algorithm of ship structures’ static and dynamic subject based on the mixed and dynamic penalty function. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(20): 70-76.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I20/70

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