Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

Antonio Llanes, Jose M. Cecilia, Antonia Sanchez, Jose M. Garcia, Martyn Amos, Manuel Ujaldon

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Ant colony optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to “real world” problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.
Original languageEnglish
Pages (from-to)1-11
JournalCluster Computing
Issue number1
Early online date27 Jan 2016
Publication statusPublished - Mar 2016


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