To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

中文翻译：

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基于结构优化的改进果蝇算法。

为了提高结构优化设计在桁架计算中的效率，提出了一种改进的果蝇优化算法。果蝇优化算法是一种新颖的群体智能算法。在标准果蝇优化算法中，难以解决高维非线性优化问题，并且容易陷入局部最优。为了克服基本果蝇优化算法的缺点，采用了免疫算法的自-非自抗原识别机制和免疫系统的学习-记忆-遗忘知识处理机制。将改进算法引入结构优化中。优化结果和与其他算法的比较表明，改进后的果蝇优化算法的稳定性明显提高，效率明显提高。这项研究为结构优化问题提供了更有效的解决方案。