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Page 10 of 19 Mai et al. Intell Robot 2023;3(4):466-84 I http://dx.doi.org/10.20517/ir.2023.37

Algorithm 1: dual-strategy ant colony optimization (DSACO)
Data: Mountain Information, Set of ants
Result: The optimal path
1 Initialization parameter information;
2 for i=1 to the size of the map do
3 if the grid is free then
4 Initialize pheromone concentration;
5 end
6 end
7 for k=1 to population size do
8 Place all ants at the start point S;
9 for iter=1 to the Maximum number of iterations do
10 for p= - to do
11 for q=- to do
12 if the position is in the map then
13 Calculate the heuristic function value according to Equation 19;
14 Calculate the state transition probability of the next node according to
Equation 14;
15 end
16 end
17 end
18 Select the next point, update the current location of ants;
19 Dynamically updated pheromones according to Equation 15;
20 if pheromone concentration ≥ then
21 pheromone concentration= ;
22 else
23 if pheromone concentration ≤ then
24 pheromone concentration= ;
25 end
26 end
27 end
28 end
29 Output the final optimal path.