改进的遗传算法在多目标物流配送路径中的应用

物流配送路径优化是物流系统设计的关键环节。针对物流配送路径问题复杂性和多约束性,提出一种改进的遗传算法——自适应免疫遗传算法(AIGA)。该算法利用一种新的免疫疫苗选择策略和免疫操作方法,使得优化过程随进化代数自适应改变,结合并列选择法对多目标物流配送路径进行优化,并给出了解决多目标物流配送路径问题的具体步骤。最后通过仿真验证,该算法的计算效率,收敛性都有明显的提高,验证了算法的实用性和有效性。

Improved Genetic Algorithm in Multi-Objective Optimization of Logistics Distribution Application

The Vehicle Routing Problem (VRP) is the key of logistics system design. According to the complexity and multiple constraint of the vehicle routing problem, this paper put forward an improved genetic algorithm - adaptive immune genetic algorithm (AIGA), the new algorithm introduces a new selection strategy vaccines and immunization methods of operation, makes the optimization process adaptive change with evolutionary algebra, and combines with parallel selection method to optimize multi-objective distribution path, and the specific steps were given for the delivery Multi-Objective Optimization of Logistics Distributio. Simulation results show that the improved algorithm high computation efficiency,and fast convergence, which verifies the practicability and effectiveness of the algorithm.