## 3 Preliminaries and Definitions
### Agdoa Algorithm
#### 4.2.2 DOA Considering Greedy Strategies
Use Algorithm 3 to initialize the dingo population position through the greedy strategy;
2. Calculate the survival probability;
3. If the survival probability is greater than the set point, jump to step 4, otherwise jump to step 9;
4. If the random value is less than P, jump to step 5, otherwise jump to step 8;
5. If the random value is less than Q, jump to step 6, otherwise jump to step 7;
6. Perform a group attack according to Equation (9) to update the agent location;
7. Perform individual persecution according to Equation (10) to update the agent location;
8. Perform the clearance strategy according to Equation (11) to update the agent location;
9. Update the position of the group with low survival rate according to Equation (12);
10. Update the fitness value and the agent location;
11. If the maximum number of iterations is not reached, update the adaptive parameters according to Algorithm 4 and repeat steps 2-10, otherwise output the optimal fitness;
```
1:Max_iter, Curve, conver_tol, conver_counter, na_min, na_max
2:vMin
3:Initialize parameters
4:1:threshold \(\leftarrow\) 0.005
5:2: converged \(\leftarrow\) false
6:3: consecutive_iterations \(\leftarrow\) 10
7:4:iteration_count \(\leftarrow\) 0
8:5: convIter \(\leftarrow\) 0
9:6:P \(\leftarrow\) 0.5
10:7:Q \(\leftarrow\) 0.7
11:beta1 \(\leftarrow\) -2 + 4 \(\times\) rand()
12:beta2 \(\leftarrow\) -1 + 2 \(\times\) rand()
13:1:nalni \(\leftarrow\) 2
14:na \(\leftarrow\) round(na_min + (na_max - na_min) \(\times\) rand())
15:12:Positions \(\leftarrow\) initialize from Algorithm 3
16:for each position i in Positions do
17: Calculate Fitness(i)
18:endfor
19:for each iteration t from 1 to Max_iter do
20:for each agent r from 1 to SearchAgent_no do
21:summary \(\leftarrow\) 0
22:if random number() < P then
23: Calculate sumatory using Attack function
24:if random number() < Q then
25: Update Agent position using strategy for group attack by Equation (9)
26:else
27: Update agent position using strategy for persecution by Equation (10)
28:endif
29:else
30: Update agent position using strategy for scavenging by Equation (11)
31:endif
32:if survival rate is below 0.3 then
33: Execute survival process to update agent position by Equation (12)
34:endif
35: Calculate Fnew
36:if Fnew <= Fitness(r) then
37: Update agent position and fitness value
38:endif
39:if Fnew <= vMin then
40: Count and update convIter
41: Update theBestVct and vMin
42:endif
43:endfor
44: Update na by Algorithm 4
45:endfor
46:return vMin
```

**Algorithm 5**AGDOA

## 5 Experimental Setup

In this section, computer simulations were used to evaluate the performance of the proposed AGDOA algorithm and BSSA algorithm. The performance of the proposed algorithms was evaluated under different system parameters in comparison with the existing schemes. ### Simulation Settings

To evaluate the performance of the algorithms, we ran the AGDOA algorithm and the BSSA algorithm using MATLAB R-2021a. The simulation device PC was configured with 16 GB of memory, and a 2.6 GHz Intel Core i7. The simulation parameters are shown in Table 2.

Figure 2: AGDOA algorithm flow chart. ### Comparative Experiments

#### 5.2.1 Comparative Experiments of BSSA

We compared the performance of the BSSA algorithm with the following methods:

1. Northern Goshawk Algorithm (NGO): NGO is a relatively new algorithm that has the advantage of diverse search strategies that may help to better explore the solution space [13];
2. Genetic Algorithm (GA): The GA performs well in dealing with discrete problems and can effectively represent and manipulate discrete decision variables through the use of binary or integer coding [38]. 3. Binary Particle Swarm Optimization Algorithm (BPSO): BPSO is suitable for discrete optimization problems and it can represent the decision variables of the problem in binary [39]. #### 5.2.2 Comparative Experiments with Agdora

We compared the performance of the AgdOA algorithm with the following methods:

1.