Wind driven dragonfly algorithm for global optimization

Abstract

SummaryDragonfly 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.