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A Memetic Algorithm Based on Decomposition and Extended Search for Multi-Objective Capacitated Arc Routing Problem

机译:一种基于分解的麦克算法和多目标电容弧路由问题的扩展搜索

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The capacitated arc routing problem is a classical NP-hard problem to solve in the field of combinatorial optimization. In recent years, due to its extensive use in our daily life, its importance has gradually emerged. Multi-objective capacitated arc routing problem (MO-CARP) is more close to real life, so it arouses widespread concern. The Multi-objective evolution algorithm based on decomposition provides a suitable frame for solving MO-CARP. In this paper, a memetic algorithm based on decomposition and extended search (ED-MAENS) is proposed to deal with MO-CARP. Firstly, decompose the MO-CARP into many single-objective sub-problems using weight vectors. Then assign represent solution for each single-objective problem. To make sure that each single-objective problems can get a reasonable represent solution, the rank conception is proposed. After that, MAENS algorithm is adopted to solve each single-objective problem using the information of its neighborhood. Finally, we proposed an extended search operator to enlarge the searching space to improve the solution quality. The new proposed algorithm is evaluated on medium and large scale instance set and experimental results demonstrate the proposed method can obtain the better non-dominated solution than compared algorithms especially on large-scale instance.
机译:电容电弧路由问题是在组合优化领域解决的经典NP难题。近年来,由于我们日常生活中广泛使用,其重要性逐渐出现。多目标电容电弧路由问题(Mo-Carp)更接近现实生活,因此它引起了广泛的关注。基于分解的多目标演化算法为求解MO-CARP提供了合适的框架。在本文中,提出了一种基于分解和扩展搜索(ED-MAENS)的迭代算法来处理MO-CARP。首先,使用重量载体将Mo-Carp分解成许多单个物理子问题。然后为每个单一目标问题分配代表解决方案。为了确保每个单目标问题可以获得合理的代表解决方案,提出了等级概念。之后,采用莫伦算法使用其邻域的信息来解决每个单一目标问题。最后,我们提出了一个扩展的搜索操作员来扩大搜索空间以提高解决方案质量。在媒体和大规模实例集中评估了新的提出算法,实验结果证明了所提出的方法可以比大规模实例的比较算法获得更好的非主导解决方案。

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