Dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

中文翻译：

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全局优化的风驱蜻蜓算法

蜻蜓算法（DA）是一种基于蜻蜓的静态和动态群体行为的新型群体智能优化算法。该算法具有结构简单，搜索能力强，易于实现，鲁棒性强的特点。但是，DA算法本身也具有不足的求解精度和较慢的收敛速度。风优化算法（WDO）具有收敛速度快，全局搜索能力强的特点。为了提高DA算法的优化性能并避免过早收敛，在以后的算法迭代计算中引入了WDO的速度，从而加快了全局最优解的收敛速度。本文提出了一种基于风驱（WDDA）的蜻蜓算法，即减少蜻蜓算法搜索的盲目性，提高求解精度和收敛速度，提高算法的整体优化性能。23个基准测试功能和一个用于优化和比较实验的工程示例。实验结果表明，WDDA算法在功能优化方面具有更好的性能。