The photovoltaic array will produce multi-peak P-U characteristics under partial shading conditions.Aiming at the problem of how to quickly and accurately realize maximum power point tracking(MPPT) to avoid a large amount of energy loss

this paper proposes an improved aquila optimization(AO) algorithm

which uses Circle chaotic mapping and reverse learning strategy to reasonably allocate the initial population position

so as to shorten the optimization time of the algorithm. At the same time

spiral optimization is carried out for the short gliding attack in aquila optimization algorithm. The whale optimization algorithm is combined to improve local optimal stagnation and convergence speed. Simulations and experiments demonstrate that

in comparison to particle swarm optimization(PSO)

whale optimization algorithm(WAO) and aquila optimization algorithm

the algorithm can search the global maximum power point with greater speed

accuracy and suppleness under both static and dynamic partial shading conditions.