旅行商问题的混合最近邻和渐进改进方法

Sandeep Dhakal, R. Chiong
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引用次数: 6

摘要

旅行商问题(TSP)是最著名的组合优化问题之一,半个世纪以来得到了广泛的研究。近年来提出的最先进的解决方案似乎都集中在自然启发的算法上。虽然这些算法中的许多都有良好的性能,但它们在计算方面相当昂贵。本文描述了一种基于最近邻算法和渐进改进算法的混合解。最近邻算法是一种简单的搜索,它可以快速产生TSP的短行程,但它的解通常不是最优的。而渐进式改进算法则是一种改进策略,它需要搜索大量与当前解接近的解,并从中得出更好的解。我们在婆罗洲岛的一些主要城镇进行了这种混合方法的实验。我们的实验表明,混合方法能够在多达100个城市/城镇中一致地产生最佳或接近最佳的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid nearest neighbour and progressive improvement approach for Travelling Salesman Problem
The Travelling Salesman Problem (TSP), one of the most famous combinatorial optimisation problems, has been widely studied for half a century now. The state-of-the-art solutions proposed in recent years seem to have focused on the nature-inspired algorithms. While good performance has been reported for many of these algorithms, they are considerably expensive in terms of computation. In this paper, we describe a hybrid solution based on the nearest neighbour algorithm and the progressive improvement algorithm. The nearest neighbour algorithm is a simple search that quickly yields a short tour for TSP, but its solution is usually not optimal. The progressive improvement algorithm, on the other hand, is an improvement strategy that needs to search through a lot of solutions that are near to the current solution, and derives a better one. We conduct experiments with this hybrid approach based on some major cities and towns in Borneo Island. Our experiments show that the hybrid approach is able to produce optimal or near-optimal results consistently for up to 100 cities/towns.
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