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Wang et al. Intell Robot 2023;3(4):538-64 I http://dx.doi.org/10.20517/ir.2023.30 Page 19 of 27

Table 3. Average number of targets found with different search coefficients

= 0.2 = 0.5 = 0.8

= 0.2 1.15 1.48 1.62
= 0.5 1.28 1.55 1.68
= 0.8 1.25 1.53 1.58

function and IGAFA is mainly discussed in the following. For the coefficients in the search information map,
set = 20, = 0.95, 1 = 0.67, 2 = 0.9asacorrespondencetotheupdatemethodofsearchinformation
map mentioned earlier. For the coefficients in the basic parameters in IGAFA, set 1 = 0.4, 2 = 0.3 and
= 0.3 as the basic configuration of the following experiments.

In this experiment, the coefficients in the objective function and the factors in IGAFA will be determined.
Firstly, considering the sensitivity to various indices and the balance between benefits and penalties, set =
1.0 as the basis of the main search benefit and set = 0.1 as the basis of the penalty for UAV motion cost. To
test the influence of the configuration with different ratios of and , we use a traditional GA as the basic
algorithm to search the moving target with the same probability distribution of the initial position as that in
the previous experiment. Table 3 shows the search result of the number of targets found at simulation step
= 100.

Table 3 indicates that the group of = 0.5 and = 0.8 has the best optimization performance. Therefore,
the coefficients are set accordingly for better detection-evasion effectiveness of adversarial games in the search
process.

Secondly, the factors in IGAFA will be determined by calculating the optimal search path for UAVs at their ini-
tial position. First of all, without considering the weight matrix during the mutation process, test the influence
of different and on the result of optimization. Figure 9 shows the best fitness under different iteration
steps with different and . It indicates that the group of = 1 and = 1 has the best optimiza-
tion performance. Therefore, the factors are set accordingly for a better evolution process of the probability
distribution of selected genes.

Next, considering the weight matrix during the mutation process, we test the influence of different on the
result of optimization. Figure 10 shows the best fitness under different iteration steps with different . It
indicates that = 0.5 has the best optimization performance. Therefore, the factor is set accordingly for a
better process of mutation.

6.3. Process and result analysis of a complete search simulation
Based on the configuration in the second experiment, randomly arrange moving targets with pre-set position
distribution and simulate the complete cooperative search process using CSMTPE with the procedures in
Table 2. Figure 11 shows the search process of a complete search simulation, including the current search path,
the search path for the next steps, and the probability map, certainty map, and detection response map at
the simulation steps = 1, = 19, and = 40, where UAV 1 finds and locks on Target 1 at step = 19 and
UAV 2 finds and locks on Target 2 at step = 40.

In the probability map in Figure 11(a), the existence probability of the target dynamically changes with the
search process, and the UAV swarm converges and aggregates the probability to the actual position of the
targets, achieving an effective search process. The certainty map in Figure 11(b) reflects the currently searched
area of the UAV swarm, avoiding repeated searches of the same area in a short time, while the lower certainty
after a long time will guide the UAVs to revisit the area to enhance the search order in the later stage of the