基于量纲蚁群优化的TSP求解方法

Pragya, M. Dutta, Pratyush
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引用次数: 10

摘要

本文描述了一种用于求解旅行商问题的分布式算法——多维蚁群优化算法。在任意群体系统(ACS)中,一组被称为蚂蚁的合作主体合作寻找tsp的最佳解决方案。蚂蚁合作使用一种间接的通信形式,这种形式是由它们在构建解决方案时沉积在TSP图边缘的信息素介导的。提出的基于基本蚁群算法的系统(维度蚁群算法)具有明确的分布策略,将整个搜索空间区域初始划分为N个超立方象限,其中N为整个搜索空间区域的维数,用于更新蚁群算法的启发式参数,提高算法在求解TSP时的性能。实验结果表明,该算法的性能优于其他标准基准测试算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TSP Solution Using Dimensional Ant Colony Optimization
This paper describes Dimensional Ant Colony Optimization (DACO), a distributed algorithm that will be applied to solve traveling salesman problem (TSP). In an Any Colony System (ACS), a set of co-operating agents called ants co-operate to find the good solutions of TSPs. Ants co-operate using an indirect form of communication that is mediated by pheromone they deposited on the edges of the TSP graph when building solutions. The proposed system (Dimensional ACO) based on basic ACO algorithm with well defined distribution strategy in which entire search space area is initially being divided into N numbers of hyper-cubic quadrants where N is the dimension of entire search space area for updation of heuristic parameter of ACO and to improve the performance while solving TSP. From our experiments, this proposed algorithm has better performance than other standard bench mark algorithms.
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