利用蚁群优化算法确定旅行商问题路线

B. P. Silalahi, Nur Fathiah, Prapto Tri Supriyo
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引用次数: 9

摘要

蚁群优化是一种用于解决组合优化问题的元启发式方法。蚁群优化算法是受现实世界中蚂蚁行为的启发,建立食物源和巢穴之间的最短路径。旅行商问题是一个最优化问题。旅行推销员问题是一个寻找从初始节点到整个节点的最小距离的问题,每个节点必须被访问一次并且必须返回到初始节点。旅行商问题是一个非确定性多项式时间完全问题。本研究讨论了用蚁群优化算法求解旅行商问题,同时也使用了精确算法。结果表明,旅行商问题案例的规模越大,所需的执行时间越长。结果还表明,蚁群优化的执行时间比精确方法的执行时间要快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes
Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the execution time of the exact method.
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