DAMS: distributed adaptive metaheuristic selection

Bilel Derbel, Sebastien Verel
Laboratoire d’Informatique Fondamentale de Lille (LIFL), Universite Lille 1, Villeneuve d’Ascq, France
Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO ’11, 2011


   title={DAMS: Distributed Adaptive Metaheuristic Selection},

   author={Derbel, B. and Verel, S.},



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We present a distributed algorithm, Select Best and Mutate (SBM), in the Distributed Adaptive Metaheuristic Selection (DAMS) framework. DAMS is dedicated to adaptive optimization in distributed environments. Given a set of metaheuristics, the goal of DAMS is to coordinate their local execution on distributed nodes in order to optimize the global performance of the distributed system. DAMS is based on three-layer architecture allowing nodes to decide distributively what local information to communicate, and what metaheuristic to apply while the optimization process is in progress. SBM is a simple, yet efficient, adaptive distributed algorithm using an exploitation component allowing nodes to select the metaheuristic with the best locally observed performance, and an exploration component allowing nodes to detect the metaheuristic with the actual best performance. SBM features are analyzed from both a parallel and an adaptive point of view, and its efficiency is demonstrated through experimentations and comparisons with other adaptive strategies (sequential and distributed).
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