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A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems

机译：基于ACO和PSO的容量车辆路径问题混合算法。

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摘要

The vehicle routing problem (VRP) is a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligence (SI) approaches, ant colony optimization (ACO) and particle swarm optimization (PSO), for solving capacitated vehicle routing problems (CVRPs). In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.
机译：车辆路径问题（VRP）是众所周知的组合优化问题。由于发现有效的车辆路线是后勤管理的重要问题，因此已经进行了数十年的研究。本文提出了一种新的混合算法，该算法基于两种主要的群体智能（SI）方法：蚁群优化（ACO）和粒子群优化（PSO），用于解决车辆拥挤的车辆路径问题（CVRP）。在提出的算法中，每个人工蚂蚁（如PSO中的粒子）都可以记住有史以来最好的解决方案。解决方案构建后，只有精英蚂蚁才能根据自己迄今为止最好的解决方案更新信息素。此外，信息素扰动方法被嵌入到ACO框架中，以克服信息素停滞的问题。选择了两组基准问题来测试所提出算法的性能。计算结果表明，与现有的群体智能方法相比，该算法性能良好。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第8期|726564.1-726564.17|共17页
  • 作者

    Yucheng Kao; Ming-Hsien Chenand; Yi-Ting Huang;

  • 作者单位

    Department of Information Management, Tatung University, 40 Chungshan N. Road, Sec. 3, Taipei 104, Taiwan;

    Department of Information Management, Tatung University, 40 Chungshan N. Road, Sec. 3, Taipei 104, Taiwan;

    Department of Information Management, Tatung University, 40 Chungshan N. Road, Sec. 3, Taipei 104, Taiwan;

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