Research Article
Multiobjective Sustainable Order Allocation Problem Optimization with Improved Genetic Algorithm Using Priority Encoding
| | Input: Pop, Pc, and Pm. | | | Output: the best solution X | | (1) | Generate a initial population (Pop) | | (2) | X the best solution in the initial Pop | | (3) | while stopping rule is not met do | | (4) | for each solution in Pop do | | (5) | Fitness (Pop) | | (6) | end for | | (7) | the best solution in the current Pop | | (8) | if is better than X, then | | (9) | X | | (10) | end if | | | // Crossover | | | // u01 is a random number between 0 and 1; Pc is a crossover probability | | | // , are two different parent individuals; C is a child solution | | (11) | if u01 < Pc, then | | (12) | selection (Pop); | | (13) | selection (Pop); | | (14) | C crossover () | | (15) | end if | | | // Mutation | | | // Pm is a mutation probability; is a parent individual | | (16) | if u01 < Pm, then | | (17) | selection (Pop); | | (18) | C mutation () | | (19) | end if | | (20) | Update the population | | (21) | end while |
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