Manchester Metropolitan University's Research Repository

    Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

    Llanes, A, Cecilia, JM, Sánchez, A, García, JM, Amos, M and Ujaldón, M (2016) Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Computing, 19. ISSN 1386-7857


    Download (1MB) | Preview


    © 2016 Springer Science+Business Media New York 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.

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Repository staff only

    Edit record Edit record