From Figures 1-5, it can be seen that MFOA has best convergence precision and stability.
So MFOA has high convergence precision and strong ability to jump out of local extreme value.
The factors that influence the performance of MFOA are population size, the maximum number of iterations, and the variation factor h.
Figure 9 is the diagram of the parameters extraction of J-A model applying MFOA.
From Section 3.2.2, it can be seen that the factor h influences the performance of the MFOA. Therefore, the influence of the factor h on the extraction performance has been discussed in this section.
So it is stated that when h = 0.9, the MFOA has good extraction performance in accuracy, stability, and simulation time.
MFOA, FOA, and PSO are used to identify the parameters of J-A model according to the experimental hysteresis loop of the nonoriented steel V3250-50A taken from literature [3].
(1) MFOA: [M.sub.s] = 1.2426 x [10.sup.6], a = 63.86, [alpha]= 11.79 x [10.sup.-5], c = 0.77, k = 60.97.
The green line (obtained by FOA) shows the biggest error is -0.1507 T at H = 6.75 A/m and the error is bigger than that of MFOA and PSO when H is becoming saturated.
From Table 2, it can be observed that the fitness value achieved by MFOA is better than the minimum fitness values achieved by PSO and FOA.
In this paper, a modified FOA is proposed and the effects of the variation factor h of MFOA are studied through five test functions.
Caption: FIGURE 9: The implementation procedure of MFOA.
