求解带时间窗车辆路径问题的混沌遗传算法

针对遗传算法随机性大、末成熟收敛等缺点,提出了将混沌搜索技术和遗传算法相耦合的混沌遗传算法来求解带时间窗的物流配送车辆路径问题(VRPTW)。该算法将混沌变量映射到优化变量的取值范围中,把得到的混沌变量进行编码生成初始种群,然后在遗传操作进行之后对优秀个体增加混沌扰动,促进种群的进化收敛速度,得到最优解。实例计算结果与其他算法比较表明,该算法在求解VRPTW问题时,搜索效率高,能以较快的速度收敛于全局最优解,为求解VRPTW问题提供了一种新方法。

Chaotic genetic algorithm for solving vehicle routing problems with time windows

Aiming at the disadvantage of big randomness and premature convergence in genetic algorithm, the paper put forward the chaotic genetic algorithms which was a combination of chaotic search technology and genetic algorithms to solve the vehicle routing problem with time windows VRPTW during the logistics and distribution. The algorithm mapped chaotic va-riables to the range of optimization variables and coded the getting variables to generate the initial population. Then, after the genetic operations, it increased chaotic disturbance to the excellent individuals, and promoted the convergence rate of populations' evolution and get the optional solution. Compared the calculation results with other algorithms show that when the algorithm solves the VRPTW problem, the search efficiency is high and it can converge in the optional solution in a fast speed and offers a new method to the VRPTW solving.