首页|Biased random-key genetic algorithms for the weighted minimum broadcast time problem

Biased random-key genetic algorithms for the weighted minimum broadcast time problem

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  • NETL
  • NSTL
  • Springer Nature
Broadcasting is an essential operation in distributed systems, with a wide range of applications. This study is focused on solving the Weighted Minimum Broadcast Time (WMBT), a problem that extends the classical Minimum Broadcast Time problem (MBT) by incorporating costs associated with each communication operation. We propose five contributions to the WMBT: (i) an integer linear programming model, (ii) two greedy algorithms, (iii) two Biased Random-Key Genetic Algorithms (BRKGAs), (iv) a lower bound algorithm, (v) a reduction rule to decrease an instance size, and (vi) a method to create instances with known optimal solutions. Our novel approaches are compared with state-of-the-art methods using large-scale synthetic instances. The experimental results demonstrate the effectiveness of our proposals. The greedy algorithms attains the best known solutions in a significant number of instances, while the two BRKGAs further enhance this performance, surpassing the greedy algorithms in many of the tested instances.

Combinatorial optimizationWeighted minimum broadcast timeMetaheuristics

Alfredo Lima、Luiz Satoru Ochi、Bruno Nogueira、Rian G. S. Pinheiro

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Institute de Computacao, Universidade Federal Fluminense, R. Passo da Patria, Niteroi, Rio de Janeiro 24210-240, Brazil||Institute de Computacao, Universidade Federal de Alagoas, Av. Lourival Melo Mota, Maceio, Alagoas 57072-900, Brazil

Institute de Computacao, Universidade Federal Fluminense, R. Passo da Patria, Niteroi, Rio de Janeiro 24210-240, Brazil

Institute de Computacao, Universidade Federal de Alagoas, Av. Lourival Melo Mota, Maceio, Alagoas 57072-900, Brazil||Programa de Pos-Graduacao em Engenharia da Computacao, Universidade de Pernambuco, Rua Benfica, Recife, Pernambuco 50720-001, Brazil

Institute de Computacao, Universidade Federal de Alagoas, Av. Lourival Melo Mota, Maceio, Alagoas 57072-900, Brazil

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2025

10.1007/s10479-025-06609-5

Annals of operations research

Annals of operations research

ISSN：0254-5330
年,卷(期)：2025.349(3)
  • 44