Algorithm1: Load balance based on GA

1. Input: $Tra\left(b_{ij}\right);B{W}_i;P\left(B_i\right);A_{ij}\left(T_{i-\delta }\right),P\left(A_{ij}\left(T_{i-\delta }\right)\right),\delta =1\cdots h;$ 2. Output: $A_{ij}\_best,fitness\_best$ 3.Create a population matrix $Population\left[N,k\right]$; 4.for$i$from 1 to N 5.$Population\left[i,:\right]$$\leftarrow \mathrm{rand} (A_{ij}$) 6. $Tr{a}_{total}\leftarrow {{\displaystyle \sum }}_{j=1}^{k}Tra\left(b_{ij}\right)\times Population\left[i,k\right]$ 7. $T\_dela{y}_{ave}\leftarrow \frac{Tr{a}_{total}}{B{W}_i\times k}$ 8. $fitness\left(i\right)\leftarrow \frac{k_1\times Tr{a}_{total}}{k_2\ast T\_dela{y}_{ave}}$ 9. end for 10.Row vectors in matrix $Population\left[N,k\right]$ sorted by fitness in descending order 11. $fitnes{s}_{ave}=\frac{1}{N}\times {{\displaystyle \sum }}_{i=1}^N fitness\left(i\right)$ 12.Perform crossover operations $Crossover \left(Population\left[i,:\right]\right)$ 13.Perform mutation operations $Mutation\left(Population\left[i,:\right]\right)$ 14.for$i$from 1 to end 15. $Tr{a}_{total}\leftarrow {{\displaystyle \sum }}_{j=1}^{k}Tra\left(b_{ij}\right)\times Population\left[i,k\right]$ 16. $T\_dela{y}_{ave}\leftarrow \frac{Tr{a}_{total}}{B{W}_i\times {{\displaystyle \sum }}_{j=1}^{k}Population\left[i,k\right]}$ 17. $fitness\leftarrow \frac{k_1\times Tr{a}_{total}}{k_2\ast T\_dela{y}_{ave}}$ 18. end for 19.Row vectors in matrix $Population\left[end,k\right]$ sorted by fitness in descending order 20.Tournament selects N rows to form a new population matrix 21.if reach the maximum evolution generation then 22. $A_{ij}\_best\leftarrow Population\left[1,:\right]$ 23. $fitness\_best\leftarrow fitness\left(Population\left[1,:\right]\right)$ 24. else 25.Go to step 11 26. end if