At the R1 observation point, we used the BeiDou IGSO C06 satellite to conduct simulation experiments; this simulation is hereafter referred to as experiment I.
Figure 5 shows the analog scene monitoring of oil slicks observed on the sea surface, and the black color in the figure represents the shape of the oil slick; the inverted triangles represent the locations of the receiver as R1 and R2; and star points represent the final SPs of C06 (IGSO) and C11 (MEO) in the two experiments.
Figure 6(a) shows the averaged scattering coefficients of both oil-slicked and clean ocean surfaces in the IGSO scenario.
Figures 7(a) and 7(b) show the corresponding scattering coefficient distributions of the two experiments, IGSO and MEO scenarios, respectively.
A comparison of Figures 8 and 9 revealed that the Doppler shift and the output power from MEO were wider and stronger, respectively, than those from IGSO. This occurred because the velocity of MEO was faster than that of IGSO in the observed period, and the height of MEO was lower than that of IGSO.
We conducted two simulation experiments of oil slicks by using the BeiDou IGSO C06 and MEO C11 satellites under the same wind conditions.
The pseudocodes of the IGSO algorithm are shown as Pseudocode 1.
In this paper, the improved glowworm swarm optimization (IGSO) algorithm is applied to the multilevel color image thresholding problem.
In this section, a large number of experiments are carried out on ten well-known color test images in order to test the performance of the IGSO algorithm for multilevel color image thresholding.
In this section, the IGSO algorithm is used for ten color test images using Otsu's method as fitness function and the results are compared with the GSO, APSO, and SaDE algorithms.
The number of thresholds, the optimal thresholds, and the optimal objective values for IGSO, GSO, APSO, and SaDE algorithms are listed in Tables 7 and 8.