Algorithm 1: PSO update operator
Input:
The individual at step t: $L^t=\{{\phi }_1^t,{\phi }_2^t,\dots ,{\phi }_h^t\}$
Output:
The updated individual at step $t+1$: $L^{t+1}=\{{\phi }_1^{t+1},{\phi }_2^{t+1},\dots ,{\phi }_h^{t+1}\}$
1 Veclocity update
2 $v^{t+1}=\omega \ast v^t+r_1\ast C_1\ast ({\mathrm{pBest}}_L^t-L^t)+r_2\ast C_2\ast ({\mathrm{gBest}}^t-L^t)$
3 Position update
4 $L^{t+1}=L^t+v^{t+1}$
5 pBest update
/∗ $pBes{t}_L$ is the best position found by individual L	∗/
/∗ or the best individual among {$L^1,L^2,\dots ,L^t\}$	∗/
6 if L is better than $pBes{t}_L$ then
7 $|pBes{t}_L\leftarrow L$
8 end
9 gBest update
/∗ $gBest$ is the best position found by the population	∗/
/∗ or the best individual among all $pBest$	∗/
10 if L is better than gBest then
11 $|gBest\leftarrow L$
12 end