Page 116 - IJOCTA-15-4

Zhang et al. / IJOCTA, Vol.15, No.4, pp.649-669 (2025)

Figure 5. Envelope spectrum of the simulated signal y(t)

Figure 6. Fitness iteration curve
Abbreviations: GJO: Golden jackal optimization; GWO: Grey wolf optimizer; WOA: Whale optimization
algorithm.

To demonstrate the superior search perfor- The stabilization of the fitness curve further con-
mance of GJO, two other algorithms—GWO and firms that the optimal parameter values have been
whale optimization algorithm (WOA)—were em- successfully identified. Although the GWO and
ployed for comparative analysis. In the experi- WOA algorithms demonstrate relatively fast con-
ments, the population size for each optimization vergence speeds, their fitness values remain sig-
algorithm was set to 50, and the maximum num- nificantly higher than those obtained by the GJO
ber of iterations was fixed at 10. Each algorithm algorithm. As evidenced in Table 1, the GJO al-
was applied to optimize the VMD parameters gorithm achieves the best fitness value, highlight-
for the simulated signal shown in Figure 4. The ing its superior capability in accurately determin-
corresponding fitness convergence curves and op- ing optimal decomposition parameters. There-
timization results are presented in Figure 6 and fore, the VMD parameters optimized using GJO
Table 1. were applied to decompose the simulated signal
shown in Figure 4. The resulting time-domain
Table 1. Optimization results of variational mode waveform and frequency spectra of the extracted
decomposition parameters using different algorithms IMF components are presented in Figure 7.
With each IMF component’s frequency dis-
Intelligent search [K, α] Fitness persed from low to high frequencies and each com-
algorithms value
ponent focused around its center frequency, the
GJO [6, 2922] 7.82522 signal decomposition findings are reasonable, as
GWO [5, 1748] 7.84541 illustrated in Figure 7. There is no discernible
WOA [6, 4000] 7.84544
frequency overlap, suggesting that modal aliasing
Abbreviations: GJO: Golden jackal optimization; is not present. The total evaluation factor for
GWO: Grey wolf optimizer; each IMF component is computed, as illustrated
WOA: Whale optimization algorithm.
in Figure 8, to effectively choose the IMF compo-
As shown in Figure 6, with the increase in nents that are sensitive to signal characteristics.
the number of iterations, the fitness value gradu- As shown in Figure 8, it is easy to identify
ally decreases, indicating that the decomposition that IMF4 and IMF2 have the largest difference.
results are steadily approaching an optimal solu- Therefore, IMF4 was chosen as the sensitive IMF
tion under the guidance of optimized parameters. component for envelope demodulation. Figure 9
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