CAJ | 학술논문

应用遗传算法对车辆路径问题（VRP）求解时，由于遗传算法在解决VRP问题时，交叉操作难以保留优秀基因片段，可能导致算法收敛较慢等问题。在一定程度上影响了遗传算法解决VRP问题的实用性。在前人的基础上，通过一种多级正向变异方法，使变异最大程度向好的方向进行，拆除基因片段中较差的基因连接并建立新基因连接，从而得到较优的新基因片段，重复一定的变异次数，让变异达到最优效果。通过实验表明多级正向变异明显提高了遗传算法解决此类问题的效率。
응용유전산법대차량로경문제（VRP）구해시，유우유전산법재해결VRP문제시，교차조작난이보류우수기인편단，가능도치산법수렴교만등문제。재일정정도상영향료유전산법해결VRP문제적실용성。재전인적기출상，통과일충다급정향변이방법，사변이최대정도향호적방향진행，탁제기인편단중교차적기인련접병건립신기인련접，종이득도교우적신기인편단，중복일정적변이차수，양변이체도최우효과。통과실험표명다급정향변이명현제고료유전산법해결차류문제적효솔。
When using genetic algorithm to solve VRP problem, a slower convergence problem may be generated because the crossover operation could not keep good genes, which affects the usefulness of genetic algorithms to solve the VRP problem to a certain extent. On the basis of our predecessors, we have created a multi-level forward mutation method which dismantles poor gene fragment connection and creates a new connection to get a better gene. A large number of experiments show that forward mutation can greatly improve the genetic algorithm to solve such problems efficiently.