融合策略改进的蝴蝶优化算法及其桁架优化

Butterfly optimization algorithm with fusion strategy improvement and its truss optimization

针对基本蝴蝶优化算法(BOA)收敛速度慢、稳定性较差的问题,提出一种融合了乌鸦搜索算法(CSA)的改进型蝴蝶优化算法(CSBOA)。CSBOA采用乌鸦搜索算法的全局搜索机制代替蝴蝶优化算法的全局搜索阶段以增强其全局探索能力,加入自适应权重,平衡算法全局与局部搜索的迭代步长,帮助蝴蝶在更大范围内寻找食物。通过12个不同特征的基准函数、1个工程设计结构优化问题对算法性能进行了实验和测试,研究表明CSBOA的收敛速度和求解精度都有明显提高,进一步验证了CSBOA可以高效解决高维基准函数和复杂工程应用问题。

To address the problems of slow convergence and poor stability of the basic butterfly optimization algorithm(BOA),an improved butterfly optimization algorithm(CSBOA)that incorporates the crow search algorithm(CSA)is proposed.The CSBOA uses the global search mechanism of the crow search algorithm instead of the global search phase of the butterfly optimization algorithm to enhance its global exploration capability,and adds adaptive weights to balance the iteration steps of the global and local search of the algorithm to help the butterfly find food in a larger area.The performance of the algorithm is experimented and tested by 12 benchmark functions with different characteristics and one engineering design structure optimization problem.The study shows that the convergence speed and solution accuracy of CSBOA are significantly improved,which further verifies that CSBOA can efficiently solve high-dimensional benchmark functions and complex engineering application problems.