A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge
Algorithm 1
The pseudo code and framework of the improved GA.
Initialize parameters which include population size Popsize, selection rate Ps, crossover rate Pc, mutation rate Pm, and the maximum number of iterations Maxgen.
Generate the initial population randomly.
Calculate each task’s depth ,
for j from 1 to n do
Sequence tasks according to tasks’ depth, and achieve the task sequence
Assign the team to each task, and achieve the chromosome Pop{j}
end for
while current generation Cg < Maxgen do
for j from 1 to n do
Decode it by greedy algorithm and calculate its completion time
Set the fitness function as
end for
Rank the individuals’ fitness value, get the max (fitness) value Val
Save the result as the best solution of current generation yy (Cg) = Val
for j from 1 to n do
Select individual by roulette wheel selection
Crossover individual by one-cut point crossover
Mutation individual
for j from 1 to n do
if r and (0, 1) <Pm then
Generate mutation position
Reset individual at position ，generate new offspring
