基于局部搜索的增强蚁群优化

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI:10.1109/ICIS.2018.8466388

Yindee Oonsrikaw, A. Thammano

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引用次数: 5

摘要

蚁群优化算法在求解车辆路径问题时经常陷入局部最优解的困境，无法逼近全局最优解。本文试图通过修改蚁群算法(蚁群算法的一个实例)来弥补这一缺陷。该改进包括新的路线构建方法、新的权值以提高每条路线的信息素密度，以及引入SA以提高局部搜索解的质量。它被称为局部搜索增强蚁群优化(EACOL)。在10个标准数据集上对EACOL算法进行了性能测试，并将其与蚁群算法和精英蚁群算法进行了性能测试，结果表明本文提出的算法在这些数据集上的性能优于这两种算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Enhanced Ant Colony Optimization with Local Search

The ant colony optimization (ACO) algorithm frequently gets trapped around local optimum solutions and does not approach the global optimum solution of vehicle routing problem. This work attempts to remedy this drawback by modifying the ant system (AS) algorithm, an instance of ACO. The modification includes a new method of route construction, new weight for improving the pheromone density of each route, and introduction of SA to improve the quality of the solutions from local search. It is called an enhanced ant colony optimization with local search or EACOL. A performance test was performed on EACOL on 10 standard datasets comparing it to those of the ant system algorithm and elitist ant system, and the results show that the proposed algorithm performs better than these two algorithms on these datasets.

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来源期刊

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)

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