# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 3 UAVs Deployment Methods for Disaster Scenarios of Different Scales
### Large Area UAVs Deployment Elitist Strategy Genetic Algorithm  
The standard genetic algorithm (SGA), proposed by [PERSON] of the United States in the 1970s, is a computational technique that utilizes mathematical methods and computer simulations to transform the problem-solving process into a series of chromosome-based genetic operations such as selection, crossover, and mutation, similar to biological evolution. Genetic algorithms can be encoded using either binary or real-number representations, and this article adopts the real-number encoding scheme. The LAESGA algorithm process is defined as shown in Algorithm 2.  
Selection operation: The common selection method for genetic algorithms is binary tournament selection; because only two individuals are compared at a time, the mechanism of this method is simple, reducing diversity. In view of the defects of the binary tournament, we made some improvements to the selection operation. First, the fitness function values of each individual in the population are calculated and ranked from large to small. Then, an elite strategy is applied to the first two ranked individuals, which are preserved and directly copied to the next generation; the remaining ranked individuals were equally divided into two groups, and the individuals with the same rank in each group were cross-operated, thus avoiding limiting the population to a local optimal solution. Since only a comparison between two individuals is involved at a time, the calculation is simple and fast. To understand the selection operation more clearly, below is a schematic of a selection operation with a chromosome number of 10, as shown in Figure 3:The chromosome before ranking is \(X=(X_{1},X_{2},\ldots,X_{\omega})\); after ranking, it becomes \(X^{*}=(X_{1}^{*},X_{2}^{*},\ldots,X_{\omega}^{*})\) and satisfies \(fit_{final}(X_{1}^{*})\geq fit_{final}(X_{2}^{*})\geq\ldots\geq fit_{final}(X_{ \omega}^{*})\).  
Crossover operation: The uniform crossover operation based on random weights is adopted. In each crossover process, two parent individuals are selected from the mating pool, denoted as \(F_{i}\) and \(F_{i+1}\) respectively. Then, the decision of whether to perform crossover is determined based on the probability of the crossover rate \(P_{\text{C}}\). If the randomly generated number is less than \(P_{\text{C}}\), the crossover operation is executed. The crossover operator is expressed as follows:  
\[C_{i}=\sigma F_{i}+(1-\sigma)F_{i+1} \tag{15}\]  
\[C_{i+1}=\tau F_{i+1}+(1-\tau)F_{i} \tag{16}\]  
\(C_{i}\) is the offspring chromosome, \(F_{i}\) is the parent chromosome, and \(\sigma\) and \(\tau\) are random values between 0 and 1. The crossover probability \(\lhd\) is set at 0.9, the mutation probability \(\rhd\) is set at 0.09, and the number of chromosomes \(\omega\) is 30.  
Mutation operation: [PERSON]'s mutation method can generate continuous variation, which allows the genetic algorithm to fine-tune and search in solution space and provides a mechanism for global search, so it is widely used in genetic algorithms for unconstrained optimization problems.  
The genetic algorithm has shown promising results in the application of UAV deployment. However, the standard genetic algorithm suffers from lengthy computation times and premature convergence. To address these limitations, LAESGA was devised to enhance the overall convergence capability of the standard genetic algorithm. Consequently, the deployment accuracy of UAVs is significantly improved, making it suitable for extensive UAV deployments.  
Figure 3: Schematic of the selected operation.