The superiority of the MAFSA is reflected not only in convergence speed, but also in the convergence accuracy.
For further effectiveness of the proposed algorithm, the MAFSA is evaluated by using this type of PV module operating under different irradiance and temperature conditions.
From Figures 11 and 12, we can see that the V-I values of the curves obtained using the identified parameters are quite consistent with the experimental data over the whole range, which means that the parameters identified by our proposed MAFSA can represent the intrinsic parameters of the PV module efficiency.
From Table 4, it is very clearly seen that the computational efficiency of the MAFSA has been improved significantly compared with that of other methods.
(5) For MAFSA, Sizepop = 30, MAXGEN = 100, Step = 0.5, Try_number = 20, Visual = 2.5, [delta] = 0.618, and PMO = 0.05.
The computational time of the MAFSA is in direct proportion to the computational complexity.
Meanwhile, the MAFSA generated variation in a relatively small range and the standard deviation are small and tolerable.