Research Article
A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem
Algorithm 1
Structure of the NSGA-II algorithm [
25].
(1) Generate the initial population of size | (2) Evaluate these solutions | (3) Sort these solutions by non domination and crowding distance | (4) Creation of the offspring population with the operators of selection, crossover and mutation | (5) Evaluate all solutions | (6) Sort the solutions of two populations: and | (7) Choose the best solutions for the new population with the remaining steps (ranking into non | dominated front, crowding distance) | (8) If the stopping criteria is satisfied then the algorithm is stopped, the obtained results are all the | solutions in the first non dominated front; repeat steps (4) to (7) otherwise |
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