Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

. 2022 Feb 24;22(5):1795. doi: 10.3390/s22051795

Algorithm 1. Pseudo-code of SSVUBA
Start SSVUBA.
1.	Input the optimization problem information: Decision variables, constraints, and objective function
2.	Set the T and N parameters.
3.	For t = 1:T
4.		$Adjust number of selected variables to update (I_{v}$ $) using Equation (3) . I_{v} \leftarrow round ((1 - \frac{t}{T}) \cdot m)$
5.		For i = 1:N
6.			For j = 1: $I_{v}$
7.				Select a population member randomly to guide the ith population member. $X_{S} \leftarrow X (S, :), S r a n d o m l y s e l e c t e d f r o m {1, 2, \dots, N} a n d S \neq i$ , is the Sth row of the population matrix.
8.				Select one of the variables at random to update. $x_{i, k_{j}}, k_{j} r a n d o m l y s e l e c t e d f r o m {1, 2, \dots, m}$ .
9.				$Calculate I using Equation (5) . I \leftarrow round (1 + r)$
10.				If $F_{s} < F_{i}$
11.					$Calculate the new status of the k_{j} th dimension using Equation (4) . x_{i, k_{j}}^{n e w} \leftarrow x_{i, k_{j}} + r \cdot (x_{s, k_{j}} - I \cdot x_{i, k_{j}})$
12.				else
13.						$Calculate the new status of the k_{j} th dimension using Equation (4) . x_{i, k_{j}}^{n e w} \leftarrow x_{i, k_{j}} + r \cdot (x_{i, k_{j}} - I \cdot x_{s, k_{j}})$
14.				end
15.			end
16.			$Calculate the objective function based on X_{i}^{n e w}$ $. F_{i}^{n e w} \leftarrow F (X_{i}^{n e w})$
17.			If $F_{i}^{n e w} < F_{i}$
18.				$Update the i th population member using Equation (6) . X_{i} \leftarrow X_{i}^{n e w}$
19.			else
20.				$Update the i th population member using Equation (6) . X_{i} \leftarrow X_{i}$
21.			end
22.		end
23.		Save the best solution so far.
24.	end
25.	Output the best obtained solution.
End SSVUBA.