## 3 Preliminaries and Definitions
### Agdoa Algorithm
#### 4.2.2 DOA Considering Greedy Strategies  
A population intelligence optimization algorithm's optimization performance is, in part, determined by how well it is initialized. The initialization of the population position of the DOA is generated in a random way. It is quite challenging to find the optimal solution around the feasible solution when the objective value is modest in relation to the data and the first solution chosen at random is considerably far from the ideal solution. The usual methods of optimization initialization are greedy algorithm initialization [35], sampling \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\stackrel{{\rightarrow}}{{x}}_{i}(t+1)\) & New location for dingoes \\ \(\stackrel{{\rightarrow}}{{\phi_{k}}}(t)\) & Subset of search agents \\ \(\stackrel{{\rightarrow}}{{x_{i}}}(t)\) & Current search agent, i.e., subset of wild dogs being attacked \\ \(\stackrel{{\rightarrow}}{{x_{*}}}(t)\) & Iteration of the best subset of dingoes so far \\ \(\stackrel{{\rightarrow}}{{x_{n}}}(t),\stackrel{{ \rightarrow}}{{x_{n}}}(t)\) & Randomly selected \(r_{1}\), \(r_{2}\) search agent, i.e., subset of dingoes, \\  & where \(r_{1}\
eq i\) \\ SizePop & Total size of the dingo population \\ \(\sigma\) & Randomly generated binary numbers, \(\sigma e\{0,1\}\) \\ \(\beta_{1},\beta_{2}\) & Randomly generated scale factor \\ \(r_{1},r_{2}\) & Random numbers generated from [1, maximum search agent size] \\  & with \(r_{1}\
eq r_{2}\); \(\stackrel{{\rightarrow}}{{x_{n}}}(t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key Notations of DOA. initialization [36], and heuristic rules initialization [37]. In practice, sampling initialization and heuristic rules initialization are often used in combination. Sampling initialization can provide diversity and help the algorithm better explore the search space, while heuristic rules initialization can provide better quality initial solutions, which helps accelerate the convergence of the algorithm and improves the quality of the final solution. The greedy algorithm constructs the solution step by step in a locally optimal way and tries to satisfy the constraints as much as possible. This method can obtain a better initial solution. We will use the greedy principle to optimize the initialization process of DOA. The pseudocode for the greedy strategy is shown in Algorithm 3. ```
Input: alloted_bs, U, punt Output: initial_profile Initialize parameters:
1: initial_profile \(\leftarrow\) Create a matrix of size (\(\texttt{U}\texttt{U}\texttt{U}\texttt{I}\texttt{x}\texttt{I}



each iteration, the optimal fitness change diff_vMin is computed, based on which the value of na is adaptively adjusted. ```
Input: Max_iter, Curve, tol, max_counter, vMin Output: Adjusted value of na based on adaptive mechanism
1: tol_counter \(\gets\) 0
2:for t \(\leftarrow\) 1 to Max_iter do
3: Calculate vMin for current iteration
4: if t \(>\) 1 then
5: Calculate diff_vMin = abs(Curve(t) \(-\) Curve(t + 1))
6:if diff_vMin \(<\) tol then
7: Increase tol_counter by 1
8:else
9: Reset tol_counter to 0
10:if tol_counter >= max_counter then
11: Decrease na
12:else
13: Increase na
14:endfor
15:return na
```
**Algorithm 4**Adjust Parameters Adaptively

The pseudo-code for the key parts of the AGDOA algorithm is reported in Algorithm 5. SearchAgents_no is the number of search agents, Max_iter is the maximum number of iterations, fobj is the fitness function, positions is the initial individual position matrix, ub and lb are the upper and lower bounds of the solution space, and vMin is the optimal fitness value. In order to facilitate an intuitive understanding, Figure 2 shows an algorithm flow chart. Figure 2 shows the process of the DOA algorithm integrating the greedy strategy to obtain the initial solution and dynamically changing the parameter adaptive strategy according to the degree of convergence, thus forming the operation flow of the AGDOA algorithm. According to Algorithm 5 and Figure 2, the specific steps of AGDOA are as follows:

1.