# _electronics_
## 3 Coded Packages Naming Lookup and Cache Placement
### Solving Multi-Objective Optimization Problems Based on the Multi-Colony Ant Colony Algorithm
Therefore, the pheromone increase under two targets is set as follows: \(U_{O_{w_{Adj}},O_{w_{Adj}}}\) and \(T_{O_{w_{Adj}},O_{w_{Adj}}}\) are the function values of the two targets after the current search is completed, \(U_{\min}\) and \(T_{\min}\) are the optimal values of the functions of the two targets currently found, and \(Q\) is the total amount of pheromones of an ant for one pathfinding. \[\Delta\tau_{\big{(}O_{w_{Adj}},O_{w_{Adj}}\big{)}}^{q}(t)=\left\{ \begin{array} []{c}Q\\ \frac{1}{|U_{O_{w_{Adj}},O_{w_{Adj}}}-U_{\min}|}\\ 0\end{array} \right. \begin{array}{c}ant\quad q\quad\text{passed}\quad by\big{(} O_{w_{Adj}},O_{w_{Adj}}\big{)}\\ \text{otherwise}\end{array} \right. \tag{13}\]  
and  
\[\Delta\tau_{\big{(}O_{w_{Adj}},O_{w_{Adj}}\big{)}}^{q}(t)=\left\{ \begin{array} []{c}Q\\ \frac{1}{|T_{O_{w_{Adj}},O_{w_{Adj}}}-T_{\min}|}\\ 0\end{array} \right. \begin{array}{c}ant\quad q\quad\text{passed}\quad by\quad \big{(}O_{w_{Adj}},O_{w_{Adj}}\big{)}\\ \text{otherwise}\end{array} \right. \tag{14}\]  
After each population completes an independent search, the searched solutions are arranged in ascending order, according to the objective function of the population, and the solution of a certain group is substituted into the objective function of another population from top to bottom. If this solution is better than the worst solution searched for by another population, and this solution is not in the Pareto solution, then we add this solution to the Pareto solution set, and update the global pheromone according to Formula (15), and \(0<e<1\). If this solution is inferior to the worst solution searched for by another population or the newly searched solution is repeated with a certain Pareto solution in the solution set, we reduce the pheromone on this path and update the pheromone according to Formula (16). \[\tau_{O_{w_{Adj}},O_{w_{Adj}}}(t+1)=\tau_{O_{w_{Adj}},O_{w_{Adj}}}(t)+e*Q \tag{15}\]  
and  
\[\tau_{O_{w_{Adj}},O_{w_{Adj}}}(t+1)=(1-\rho)\tau_{O_{w_{Adj}},O_{w_{Adj}}}(t) \tag{16}\]  
The process of solving the satellite's coded package placement based on the multi-population ant colony algorithm is shown in Algorithm 2. ```
1: set the number of algorithm iterations \(N_{c}\), clear the taboo visited, and initialize parameters \(\alpha\), \(\beta\), \(\rho\), and \(Z\)
2: initialize the pheromone \(\tau_{O_{w_{\alpha,i}},O_{w_{\alpha,i}}}(t)\) under the backhaul link load target and the content acquisition, delay the target, respectively
3: place \(Z\) ants of the two populations at the starting node, respectively
4:for\(i\gets 1\) to \(Z\)do
5: set the taboo table visited index \(s=1\)
6: put the starting code block number node of the ith ant in the table, visited\({}_{i}\)
7: select the next number of node-coded packages to move forward according to the formula
8: move the ant to the selected node and insert the node into the table visited\({}_{i}\)
9:if the current node is the destination node then
10:\(s=s+1\)
11:else
12: select the next node
13:endif
14:endfor
15: locally update the pheromones on each path according to Formula (12)
16:for\(s=1\) to \(Z\)do
17: read the full path of \(Z\) nodes in the visited table of the two populations
18: rank the solutions searched for by the ant colony in ascending order, based on the optimization objectives of the population
19:if this solution is better than the worst solution searched for by another population and the solution is not in the Pareto solution then
20: add this solution to the Pareto solution set, and update the pheromone according to Formula (15)
21:endif
22:if this solution is inferior to the worst solution found by another population or this solution is already in the Pareto solution then
23: update the pheromone according to Formula (16)
24:endif
25:endfor
26: set \(N_{c}=N_{c}+1\)
27:if\(N_{c}<N_{\text{max}}\)then
28: clear the table visited, go to step2
29:else
30: the solution set Pareto
31:endif
```  
**Algorithm 2** Algorithm for solving the satellite's coded package placement based on the multi-colony ant colony.