Research Article
Research on the Optimization of Cross-Border Logistics Paths of the “Belt and Road” in the Inland Regions
| | Input: Number of individuals in the population, NUM; Maximum evolutionary generation, G; | | | Output: The population that completes the optimization is the pareto optimal solution; | | (1) | /∗ Initial population ∗/ | | (2) | while = 0. ≤ G do | | (3) | population: = population + offspring;/∗ Father and son merged ∗/ | | (4) | levels: = ndSort (population, NUM);/∗ Non-dominant ranking ∗/ | | (5) | distance: = crowdis (population, levels);/∗ Calculate the crowded distance ∗/ | | (6) | population, FitnV: = argsort (lexsort ([dis-levels]));/∗Calculate fitness ∗/ | | (7) | until number of population < NUM | | (8) | return population | | (9) | /∗ Start to evolve ∗/ | | (10) | offspring: = population|selecting (population, FitnV, NUM)|;/∗Select individuals to participate in evolution ∗/ | | (11) | offspring: = recOper (offspring);/∗ Simulated binary crossover ∗/ | | (12) | offspring: = mutOper (offspring);/∗ Polynomial mutation ∗/ | | (13) | population: = reinsertion (population, offspring);/∗Reinsert to get a new generation of population ∗/ | | (14) | : = + 1 | | (15) | end do | | (16) | return population |
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