# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Offspring Parameter Generation  
The offspring parameter generation aims to generate offspring solutions based on the initial UAV's location and buoy transmit power, which contains two main steps, i.e., mating selection and variation operation, as shown in Algorithm 3. The mating selection should choose the parents from the neighborhood as much as possible because each solution is specified by a weight vector and associated with a sub-region. Therefore, the neighborhood solutions can be easily chosen from their sub-regions with the current weight vector. The mating parents are randomly chosen based on the initial UAV's location \(\mathbf{l^{(0)}_{U}}\) and buoy transmit power \(\mathbf{p}^{(0)}\) to avoid any associated solutions in the selected sub-regions. Moreover, according to [54], selecting the mating parents in a low probability \(1-\delta\) from all candidates can enhance the exploration ability. Finally, the selected parent parameter is employed to generate the offspring solutions \(\mathbf{l^{off}_{U}}\) and \(\mathbf{p^{off}}\) based on the method of polynomial mutation and simulated binary crossover.
```
1:Parent populations of \(I_{\mathbf{U}}^{(t+1)}\) and \(\mathbf{p}^{(t+1)}\).
2:Obtain the sub-region associated with offspring solution \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{off}\) based on Equation (15).
3:\(I_{\mathbf{U}}^{hp}\leftarrow I_{\mathbf{U}}^{(t)}\cup\{I_{\mathbf{U}}^{off}\}\); \(\mathbf{p}^{hp}\leftarrow\mathbf{p}^{(t)}\cup\{\mathbf{p}^{off}\}\).
4:\(\{I_{\mathbf{U}}^{is},\mathbf{p}^{is}\}\leftarrow\emptyset\).
5:for\(I_{\mathbf{U}}\in I_{\mathbf{U}}^{hp};\mathbf{p}\in\mathbf{p}^{hp}\)do
6:if\(CV(I_{\mathbf{U}},\mathbf{p})>0\)then
7:\((I_{\mathbf{U}}^{is},\mathbf{p}^{is})\leftarrow\{I_{\mathbf{U}}^{is},\mathbf{p}^{is}\}\cup\{I_ {\mathbf{U}},\mathbf{p}\}\)
8:endif
9:endfor
10:Update the non-domination level structure of \(I_{\mathbf{U}}^{hp}\) and \(\mathbf{p}^{hp}\).
11:if the last non-domination level contains only one solution, not all solutions in \(I_{\mathbf{U}}^{hp}\) and \(\mathbf{p}^{hp}\) are non-dominated from each other and \(|\psi_{l}|>1\)then
12:\(I_{\mathbf{U}}^{(t+1)}\leftarrow I_{\mathbf{U}}^{hp}\backslash I_{\mathbf{U}}^{a};\mathbf{p}^{(t +1)}\leftarrow\mathbf{p}^{hp}\backslash\mathbf{p}^{a}\).
13:endif
14:endif
15:if the last non-domination level contains more than one solution, not all solutions in \(I_{\mathbf{U}}^{hp}\) and \(\mathbf{p}^{hp}\) are non-dominated from each other and \(|\psi_{h}|>1\)then
16: Obtain \(I_{\mathbf{U}}^{u}\) and \(\mathbf{p}^{\prime}\) based on (18).
17:\(I_{\mathbf{U}}^{(t+1)}\leftarrow I_{\mathbf{U}}^{hp}\backslash I_{\mathbf{U}}^{\prime};\mathbf{p}^{(t +1)}\leftarrow\mathbf{p}^{hp}\backslash\mathbf{p}^{\prime}\).
18:endif
19:Case 3:
20:if Not the cases mentioned in case 1 and case 2. then
21: Obtain \(I_{\mathbf{U}}^{u}\) and \(\mathbf{p}^{\prime}\) based on (19).
22:\(I_{\mathbf{U}}^{(t+1)}\leftarrow I_{\mathbf{U}}^{hp}\backslash I_{\mathbf{U}}^{\prime};\mathbf{p}^ {(t+1)}\leftarrow\mathbf{p}^{hp}\backslash\mathbf{p}^{\prime}\).
23:endif
24:End Case
25:Use the non-domination level update the non-domination level structure of \(I_{\mathbf{U}}^{(t+1)}\) and \(\mathbf{p}^{(t+1)}\).
```  
**Algorithm 5** Update Parent Population of Parameters