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首页> 外文期刊>Evolutionary computation >A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem
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A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem

机译：具有遗传编程的预测 - 反应性和合作协作参与的不确定电容弧路由问题

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摘要

The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality and robust baseline task sequence, and the other is to design an effective recourse policy to adjust the baseline task sequence when it becomes infeasible and/or ineffective during the execution. Existing studies typically only tackle one challenge (the other being addressed using a naive strategy). No existing work optimises the baseline task sequence and recourse policy simultaneously. To fill this gap, we propose a novel proactive-reactive approach, which represents a solution as a baseline task sequence and a recourse policy. The two components are optimised under a cooperative coevolution framework, in which the baseline task sequence is evolved by an estimation of distribution algorithm, and the recourse policy is evolved by genetic programming. The experimental results show that the proposed algorithm, called Solution-Policy Coevolver, significantly outperforms the state-of-the-art algorithms to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Through further analysis, we discovered that route failure is not always detrimental. Instead, in certain cases (e.g., when the vehicle is on the way back to the depot) allowing route failure can lead to better solutions.
机译：不确定的电容电弧路由问题对于其在现实世界中的广泛应用方面具有重要意义。在不确定的电容电弧路由问题中，实时实现任务需求和旅行成本的变量。这可能导致预定义的解决方案变得无效和/或不可行。解决这个问题有两个主要挑战。一个是获得高质量和强大的基线任务序列，另一个是设计有效的追索策略，以便在执行期间变得不可行和/或无效时调整基线任务序列。现有研究通常只能解决一个挑战（使用天真策略解决另一个挑战）。没有现有工作同时优化基线任务序列和追索政策。为了填补这一差距，我们提出了一种新的主​​动反应方法，它代表了基准任务序列和追索政策的解决方案。这两个组分在合作的共区框架下进行了优化，其中通过分发算法的估计来演化基线任务序列，并通过遗传编程演变的追求政策。实验结果表明，所提出的算法称为解决方案 - 政策协作，显着优于UGDB和UVAL基准实例的不确定电容弧路由问题。通过进一步的分析，我们发现途径失败并不总是有害的。相反，在某些情况下（例如，当车辆在回到仓库的路上时）允许路线故障可能导致更好的解决方案。

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  • 来源
    《Evolutionary computation》 |2020年第2期|289-316|共28页
  • 作者

    Liu Yuxin; Mei Yi; Zhang Mengjie; Zhang Zili;

  • 作者单位

    Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China|Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China|Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand;

    Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand;

    Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand;

    Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China|Deakin Univ Sch Informat Technol Locked Bag 20000 Geelong Vic 3220 Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Capacitated arc routing problem; cooperative coevolution; hyper-heuristics; genetic programming;

    机译：电容电弧路由问题;合作协调;超启发式;遗传编程;

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