Li Yancang, Han Muxuan
College of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, Handan, Heibei Province, China.
College of Civil Engineering, Hebei University of Engineering, Handan, Heibei Province, China.
Brain Inform. 2020 Feb 16;7(1):1. doi: 10.1186/s40708-020-0102-9.
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.
为提高桁架计算中结构优化设计的效率,提出了一种改进的果蝇优化算法用于桁架结构优化。果蝇优化算法是一种新型群体智能算法。在标准果蝇优化算法中,难以解决高维非线性优化问题且容易陷入局部最优。为克服基本果蝇优化算法的缺点,采用了免疫算法的自身-非自身抗原识别机制和免疫系统学习-记忆-遗忘知识处理机制。将改进算法引入结构优化中。优化结果及与其他算法的比较表明,改进果蝇优化算法的稳定性明显提高,效率显著提升。本研究为结构优化问题提供了一种更有效的解决方案。