## 4 Simulation Experiment and Discussion
### Methods Configuration and Parameter Selection  
Parameter selection of the CC-MPSO algorithm is mainly derived from the recommendations in the literature [17], in conjunction with trial and error. Compared to the standard PSO algorithm, CC-MPSO has multiple swarms, and the number of swarms depends on the number of UAVs used for searching. The swarm scale remains consistent with the standard PSO, MPSO algorithm. The inertial weight \(\omega\), cognitive coefficient, and social coefficient \(c_{2}\) of different swarms use the same values. For the velocity clamping constant \(S_{k}v_{\max}\), a problem-dependent approach is used to select the velocity clamping constant of each swarm, whose magnitude is associated with the input space dimension [23]. The size of the swarm convergence constant has a significant impact on the performance of the algorithm. To optimize this parameter, we gradually increment its value in the range of \([0,1]\) by trial-and-error experiments to determine the value at which optimal performance can be achieved.  
For comparison purposes, five widely used swarm intelligence algorithms were also tested in six scenarios. These include the Particle Swarm Algorithm (PSO) [24] and Ant Colony Algorithm (ACO) [25], as well as the improved algorithms MPSO [26], ECPO-ME [10] for PSO, and RI-MAC [15] for ACO. The above algorithms were chosen because they are extremely competitive and have been applied in work related to multi-UAV target search. The list of algorithms along with their abbreviation, main reference and operational parameters are given in Table 1. The population sizes for ACO, ECPO-ME, RI-MAC, and CC-MPSO are all set to 100. However, the PSO and MPSO is set to 500 due to poor performance in simulation and difficulties in convergence when the population size is set too low. The other crucial parameters of the algorithm are set from its original references. To mitigate the inherent instability and uncertainty of a single experiment, we conducted 100 experiments for each scenario using Monte Carlo simulation. In each experiment, the algorithms were run for a maximum of 200 iterations. The results of these experiments were then subjected to statistical analysis. The specific discussions are in Section 4.3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Abbreviation** & **Main Reference** & **Parameters** \\ \hline Particle Swarm Optimization & PSO & [28] & Population size = 500, Inertia weight = 0.8 cognitive factor = 2.5, social factor = 2.5. \\ Ant colony optimization & ACO & [15] & Colony size = 100, Pheromone importance factor = 1, Pheromone Evaporation Rate =0.85. \\ Motion-encoded Particle Swarm Optimization & MPSO & [13] & Population size = 500, Inertia weight = 1 at the damping rate of 0.98 cognitive factor = 2.5, social factor = 2.5. \\ Motion-encoded electric charged particles optimization & ECPO-ME & [14] & Number of ECPs = 100, number of ECPs in interaction = 3, size of the archive pool = 100. \\ Rule-inspired multi ant colony optimization & RI-MAC & [19] & Population size = 100, the number of ant populations corresponding to the UAV swarm, Initialization of pheromone based on target prior probability. \\ Cooperative Coevolution & & & Population size = 100, inertia factor = 0.6, cognitive factor = 1.8, social factor = 1.8, velocity clamping factor = 0.2, Convergence constant = 0.2. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operational parameters of comparing algorithms.