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船舶强框架序贯代理模型辅助遗传优化方法

Chinese Journal of Ship Research(2021)

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Abstract
[目的]共同结构规范(CSR)要求下的船舶强框架结构优化存在约束条件多、计算耗时、可行性判断复杂的特点.应用静态代理模型辅助优化算法求解该问题时,因其关注的是模型的整体预测精度,故在样本容量较小的情况下无法保证关键区域的模型预测精度.针对该问题,提出基于序贯代理模型辅助遗传算法的强框架优化方法.[方法]首先,分析CSR对强框架结构的约束要求,根据约束类型,将原始的675条约束缩减为2条积极约束,再对目标函数和约束函数建立代理模型.然后,基于可行性准则,利用遗传算法对代理模型进行优化求解,得到优化解后,计算优化解的真实响应并更新代理模型,再利用期望可行性函数(EFF)准则更新约束代理模型,提高代理模型在约束边界上的精度,如此迭代求解多次,最终得到满足约束条件的全局最优解.[结果]强框架优化结果显示,所提序贯代理模型算法能够在相同,甚至更少的计算资源下得到优于基于静态代理模型优化算法的优化解,最终实现设计区域减重达15.55%.[结论]提出的序贯代理模型算法显著优于静态代理模型算法,在复杂约束下的船舶结构优化问题上有着较好的应用价值.
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Key words
strong frame,structural optimization,sequential surrogate model,expected feasibility function,genetic algorithm (ga),common structural rules (csr)
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