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首页> 外文会议>International Symposium on Intelligence Computation amp; Applications(ISICA'2007); 20070921-23; Wuhan(CN) >An Evolutionary Algorithm for Dynamic Multi-Objective TSP
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An Evolutionary Algorithm for Dynamic Multi-Objective TSP

机译：动态多目标TSP的进化算法

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摘要

Dynamic multi-objective TSP (DMOTSP), a new research filed of evolutionary computation, is an NP-hard problem which comes from the applications of mobile computing, mobile communications. Currently, only a small number of literatures related to the research of static multi-objective TSP and dynamic single objective TSP. In this paper, an evaluation criterion of the algorithms for DMOTSP called Paretos-Similarity is first proposed, with which can evaluate the Pareto set and algorithms' performance for DMOTSP. A dynamic multi-objective evolutionary algorithm for DMOTSP, DMOTSP-EA, is also proposed, which embraces an effective operator, Inver-Over, for static TSP and dynamic elastic operators for dynamic TSP. It can track the Pareto front of medium-scale dynamic multi-objective TSP in which the number of cities is between 100 and 200. In experiment, taking CHN144+5 with two objectives for example, the algorithm is tested effective and the evaluation criterion, Paretos-Similarity, is available.
机译：动态多目标TSP（DMOTSP）是进化计算的一项新研究，它是一个NP难题，来自移动计算，移动通信的应用。目前，只有很少的文献涉及静态多目标TSP和动态单目标TSP的研究。本文首先提出了一种针对DMOTSP的算法评估标准，称为Paretos-Similarity，利用该评估标准可以评估DMOTSP的帕累托集和算法性能。还提出了一种针对DMOTSP的动态多目标进化算法DMOTSP-EA，该算法包含一个有效的算子Inver-Over（用于静态TSP）和动态弹性算子（用于动态TSP）。它可以跟踪城市数量在100到200之间的中型动态多目标TSP的Pareto前沿。在实验中，以具有两个目标的CHN144 + 5为例，该算法被测试有效，评估标准为：可以使用Paretos-Similarity。

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