## 5 Simulation Experiment and Analysis
### Algorithm Performance Analysis  
The performance of the four algorithms was compared through a comparative experiment on comprehensive cooperative evaluation, average reward, and the success rate of the UAV path, based on Section 5.1. The analysis results of the three comparative experiments are presented below. The comparison of success rates among the four algorithms in Scenario 2 and Scenario 3 is illustrated in Figure 8. As the UAV's understanding of the environment deepens, all four algorithms are capable of successfully generating an optimal flight path to reach the predicted target point. However, as depicted by result in Figure 7(a), after conducting multiple experiments about the complex scenario 2, the success rate of KD-SAC is 73.63%, while SAC, RI-MAC, and PSO experience is 56.36%, 43.75%, and 40.12%, respectively. The KD-SAC algorithm outperforms SAC, PSO, and ACO algorithms significantly in terms of planning success rate. The results presented in Figure 7(b) demonstrate that, when compared to scenario 2, the success rate of KD-SAC decreases to 71.83%, SAC decreases to 53.26%, and RI-MAC and PSO decrease to 33.35% and 29.21%, respectively. In the face of complex environments with four UAVs, the proposed KD-SAC algorithm maintains a high planning success rate, thereby confirming its superiority and suitability for addressing the problems outlined. The increase in the learning rate enhances the learning speed of KD-SAC. Moreover, as the number of iterations increases, the action template within the knowledge base executes optional action for UAVs in each moment, which improves the learning efficiency of KD-SAC. Consequently, KD-SAC successfully generates an optimal path for a UAV from its initial position to its target position. The comprehensive cooperation evaluation serves as a crucial metric for assessing the effectiveness of UAV cooperation search paths. By comparing the comprehensive cooperative evaluation indexes across Scenarios 2 and Scenarios 3, the performance of the proposed KD-SAC algorithm is validated. Figure 9 illustrates the experimental comparison among four algorithms. In Figure 8(a),b, both Scenario 2 and Scenario 3 represent cooperation evaluations involving two UAVs. In Scenario 2, the four algorithms exhibit distinct differences in cooperation evaluation within a complex task area environment, with the KD-SAC algorithm demonstrating a higher index value compared to the other three algorithms. Figure 8(b) illustrates Scenario 3 where four UAVs are engaged in cooperative tasks. As the number of UAVs increases, their exploration of environmental information accelerates, and the expansion of the knowledge base is also accelerating. The shared knowledge base of UAV swarms enables each UAV to access the action template of the entire cluster, thereby fostering enhanced synergy among UAVs. Consequently, the KD-SAC algorithm outperforms other algorithms by achieving a higher index value. This indicates that the algorithm proposed in this paper showcases superior cooperation performance in search path planning. Figure 7: Comparison of optimal path planning generated by four algorithms in Scenario 3. Figure 8: Comparison of the success rate of path planning in two scenarios of four algorithms. Figure 9: Path comprehensive cooperation evaluation of four algorithms in two scenarios. To further demonstrate the effectiveness of the proposed algorithms in search path planning, we assessed the path planning outcomes of different algorithms based on the average reward value, which serves as a measure for describing the target search earnings of UAVs. The initial state of the KD-SAC algorithm differs in Scenario 2 and Scenario 3 due to its requirement for environmental information comprehension and making decisions based on the knowledge base. Therefore, the average reward value in the initial state does not impact subsequent evaluations.