A Genetic Algorithms Approach for Inverse Shortest Path Length Problems

António Leitão, Adriano Vinhas, P. Machado, Francisco C. Pereira
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引用次数: 4

Abstract

Inverse Combinatorial Optimization has become a relevant research subject over the past decades. In graph theory, the Inverse Shortest Path Length problem becomes relevant when people don't have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.
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求解最短路径长度逆问题的遗传算法
逆组合优化在过去的几十年里已经成为一个相关的研究课题。在图论中,当人们无法获得弧的实际成本,并想要推断它们的值,以便系统有一个特定的结果时,例如节点之间的一条或多条最短路径,逆最短路径长度问题就变得相关了。已经提出了几种方法来解决这个问题,依赖于不同的方法,并提出了几种应用。本研究探索了一种基于遗传算法的创新进化方法。提出了两种场景和相应的表示,并进行了实验来测试它们对不同图形特征和参数的反应。他们的行为和差异进行了彻底的讨论。该结果支持进化算法可能是解决逆最短路径问题的可行场所。
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