  We present a distributed generic algorithm called DAMS dedicated to adaptive
optimization in distributed environments. Given a set of metaheuristic, 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 node to decide distributively what
local information to communicate, and what metaheuristic to apply while the
optimization process is in progress. The adaptive features of DAMS are first
addressed in a very general setting. A specific DAMS called SBM is then
described and analyzed from both a parallel and an adaptive point of view. 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. The efficiency of
BSM-DAMS is demonstrated through experimentations and comparisons with other
adaptive strategies (sequential and distributed).
