基于双种群混合遗传算法的车辆调度问题研究

Research on Vehicle Scheduling Problem Based on Hybrid Genetic Algorithm with Two Populations

车辆路径优化问题影响着企业的生存发展,对于企业至关重要。传统遗传算法容易陷入局部最优解,算法求解性能有待改善。针对该问题,文章提出了求解车辆路径优化问题的双种群混合遗传算法。算法在进化过程中采用两个遗传种群,分别选取不同的交叉和变异概率进行遗传操作。在每一次迭代结束时,将两个种群中的优秀个体进行互换,打破种群间的平衡。同时,为了提高种群质量,文章还采用2-opt算子对种群中的最优个体进行了优化。仿真实验表明,文章所提的算法具有更好的求解性能。

Vehicle routing problem is very important for the survival and development of enterprises. The traditional genetic algorithm is easy to fall into local optimal solution, and the performance of the algorithm needs to be improved. To solve the problem, a hybrid genetic algorithm with two populations was proposed to solve the vehicle routing problem. In the process of evolution, two genetic populations were used to carry out genetic operations with different crossover and mutation probabilities. At the end of each iteration, the best individuals in the two populations were exchanged, and the balance of the population was broken. At the same time, in order to improve the quality of the population, the 2-opt operator was used to optimize the best individual of the population. The simulation experiments show that the proposed algorithm has better performance.