GPU上无容单分配集线器定位问题的并行遗传算法

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) Pub Date : 2016-11-01 DOI:10.1109/AICCSA.2016.7945636

A. Benaini, Achraf Berrajaa, J. Boukachour, M. Oudani

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引用次数: 1

摘要

提出了一种基于GPU集群的并行遗传算法(GA)，用于解决无容量单分配集线器定位问题。遗传算法采用二进制和整数编码，并采用适合该问题的遗传算子。我们的遗传算法通过在中间节点初始定位集线器来改进。在我们的实现中，我们使用GPU的能力来并行计算几个初始解决方案，改变集线器的数量。得到的实验结果在所有基准测试中与最知名的解决方案进行了比较。它们表明，我们的方法在解决方案质量和执行时间方面优于大多数知名的启发式方法。它还允许解决以前未解决的实例问题。

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Parallel genetic algorithm for the uncapacited single allocation hub location problem on GPU

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation Hub Location problem. The GA uses binary and integer encoding with genetic operators adapted to this problem. Our GA is improved by initially locating hubs at middle nodes. In our implementation we use the power of the GPU to compute in parallel several initial solutions, varying the number of hubs. The obtained experimental results compared with the best known solutions on all benchmarks. They show that our approach outperforms most well-known heuristics in terms of solution quality and time execution. Also it allowed to solve instances problem unsolved before.

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2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)

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