A Many-Objective Evolutionary Algorithm Based on Dual Selection Strategy

. 2023 Jul 1;25(7):1015. doi: 10.3390/e25071015

Algorithm 4 Dual selection

Require:
R (combined population), N (population size)
Ensure:
O (final population)
1:
$O_{T}$ = ⌀
2:
R=Objective space Initialization(R)
3:
Calculate convergence value $I_{c}$ of all individuals in R by Equation (2)
4:
Put the individual $s = a r g m i n (I_{c})$ to $O_{T}$
5:
Non-dominated sorting of all individuals in R and get a set $R_{N}$
6:
$While$ $| O_{T} |$ < N $do$
7:
Calculate diversity value $I_{d_{x^{i} \in R}} = {max}_{x^{j} \in O_{T}} d (x^{i}, x^{j})$ by Equation (4)
8:
Set the $I_{d}$ value of each corner individual to +∞
9:
Descending order according to $I_{d_{x^{i} \in R}}$ value of all individuals
10:
Find s= arg min( $I_{c}$ ) in top ranked N- $| O_{T} |$ individuals
11:
Remove s from R
12:
$End While$
13:
$O_{T} = O_{T} \cup$ $R_{N}$
14:
O = PC strategy ( $O_{T}$ )
15:
return (O);