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Page 24 of 27 Wang et al. Intell Robot 2023;3(4):538-64 I http://dx.doi.org/10.20517/ir.2023.30

Table 5. Average execution time by typical optimization algorithms
= 10 = 20
Number of UAVs
PE-GA ME-PSO OURS PE-GA ME-PSO OURS
= 3 2.8s 2.6s 3.1s 4.9s 4.8s 5.1s
= 6 4.6s 4.5s 4.8s 7.8s 7.5s 8.5s

Table 6. Average number of targets found using typical optimization algorithms at simulation step = 100
= 3 = 6
Number of targets
PE-GA ME-PSO OURS PE-GA ME-PSO OURS
= 1 1* 0.43 1* —
= 2 1.68 0.85 1.83 1.95 1.18 2*
= 3 — 2.58 1.35 2.75
= 4 — 3.23 1.85 3.58
”*” represents that all targets are found

Table 7. Average number of targets found using different search methods at simulation step = 100
= 3 = 6
Number of targets
Greedy search Parallel search CSMTPE Greedy search Parallel search CSMTPE
= 1 0.93 0.60 1* —
= 2 1.65 1.23 1.83 2* 1.38 2*
= 3 — 2.40 2.00 2.75
= 4 — 3.13 2.48 3.58
”*” represents that all targets are found

because of the added improvement methods, while the execution time of the three algorithms is roughly the
same.

We apply different algorithms to the search scenarios set in the third experiment and simulate the coopera-
tive search using CSMTPE. The results of the average number of targets found using different optimization
algorithms at simulation step = 100 are shown in Table 6.

Table 6 indicates that the ME-PSO has a significantly smaller number of targets found compared to other
algorithms, while the search result using PE-GA is slightly worse than that using IGAFA. This is consistent
with the conclusion obtained in Figure 15, proving that IGAFA is more suitable for solving such problems.

6.5. Comparison of different search methods
We now compare the CSMTPE proposed in this paper with traditional greedy search method and parallel
search method. The greedy search method calculates each grid cell within the motion range of each UAV in
each simulation step according to Equation (10) with = 1, and selects the cell with the minimum objective
function value as the cell to be searched in the next simulation step. The parallel search method refers to the
parallel search of targets by each UAV in the mission area, achieving a coverage search. Table 7 shows the
search results using different search methods at simulation step = 100 under the same configuration in the
third experiment, and Figure 16 shows the combination result of targets found at simulation steps = 25,
= 50, and = 100.

Table 7 indicates that CSMTPE has more targets in comparing search methods. Parallel search does not con-
sider the initial distribution information of the target, and the search range is too rough, resulting in poor
search efficiency. Greedy search has a similar search rate to CSMTPE in Case I and Case III, but overall, the
number of targets found is lower than CSMTPE.

As shown in Figure 16, when using the greedy search method, the number of targets found is significantly