In this study, safety margin explicit equation has been established using random variables (i.e., the engineering conditions, structure parameters, structural strength and external load), and the genetic algorithms (GA) - based structural reliability optimization design has been addressed subsequently. Though the conventional adaptive GA can change automatically with fitness, it is still not unsatisfactory in sufficiently improving the algorithm convergence speed, especially for complex structures. This paper presents an improved adaptive technology termed as the distant relative genetic algorithm (DRGA), in which the distant relative pointer and immunity operators can effectively improve the search performance of the GA. In early evolution, by means of cross controlling and avoiding pairing between individuals with the same genes, the methodology prevents the isogenic individuals expanding locally. Besides, the revised algorithm is able to jump out of the local optimal solution, thus ensuring the realization of a fast global convergence. An example based on wing box structure optimization has been demonstrated using the improved method, and the calculation results show that this strategy makes the GA more effective in dealing with the constraint optimization issues.

中文翻译：

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基于远相关遗传算法的结构可靠性优化

本研究利用随机变量（即工程条件、结构参数、结构强度和外载荷）建立了安全裕度显式方程，随后提出了基于遗传算法（GA）的结构可靠性优化设计。传统的自适应遗传算法虽然可以随适应度自动变化，但在充分提高算法收敛速度方面仍不能令人满意，尤其是对于复杂结构。本文提出了一种改进的自适应技术，称为远相关遗传算法（DRGA），其中远相关指针和免疫算子可以有效提高遗传算法的搜索性能。在早期进化中，通过交叉控制和避免相同基因个体之间的配对，该方法可防止同基因个体在本地扩展。此外，修改后的算法能够跳出局部最优解，从而保证了快速全局收敛的实现。使用改进方法对基于翼盒结构优化的实例进行了验证，计算结果表明该策略使遗传算法更有效地处理约束优化问题。