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URN etd-0713112-215306 Statistics This thesis had been viewed 2212 times. Download 858 times. Author Yi-Ting Huang Author's Email Address No Public. Department Information Management Year 2011 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 35 Title A Hybrid ACO Algorithm for Capacitated Vehicle Routing Problems Keyword ant colony optimization swarm intelligence particle swarm optimization vehicle routing problems vehicle routing problems particle swarm optimization swarm intelligence ant colony optimization Abstract 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 (CVRP). 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. Advisor Committee Yucheng Kao - advisor
Ching-Jung Ting - co-chair
Ming-Hsien Chen - co-chair
Files Date of Defense 2012-07-02 Date of Submission 2012-07-16