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Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization
Authors
A Delévacq
A Martin
+36 more
Antonia Sánchez
Antonio Llanes
B Yu
BR Ke
DE Goldberg
DE Goldberg
DS Johnson
E Alba
E Lawler
G Michell De
G Reinelt
G Rozenberg
J Nickolls
J Shalf
JM Cecilia
JM Cecilia
José M. Cecilia
José M. García
Komarudin
M Dorigo
M Dorigo
M Dorigo
M Dorigo
M Dorigo
M Manfrin
M Pedemonte
Manuel Ujaldón
Martyn Amos
MP Garcia
R González
R Rahman
RSS Chang
T Stutzle
T Stützle
W Wolf
Y Chen
Publication date
1 January 2016
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
© 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
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