Efficient approaches for the Flooding Problem on graphs

Silva, André Renato Villela da; Ochi, Luiz Satoru; Barros, Bruno José da Silva; Pinheiro, Rian G. S.

doi:10.1007/s10479-018-2796-0

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Our approach based on BRKGA is motivated by the success of random‐key algorithms on similar broadcast/flooding problems (Resende, 2011; da Silva et al., 2020), network problems (Reis et al., 2010), and other problems involving transmission (Brandão et al., 2017). To show the effectiveness of the proposed BRKGA, we carry out experiments on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies.…”

Section: Introductionmentioning

confidence: 99%

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Lima

Aquino

Nogueira

et al. 2022

Int Trans Operational Res

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The Minimum Broadcast Time (MBT) is a well‐known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large‐scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random‐Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state‐of‐the‐art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best‐known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods.

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