DAMS：分布式自适应元启发式选择
DAMS: Distributed Adaptive Metaheuristic Selection
摘要：
我们提出了一个分布式的遗传算法称为DAMS专用于自适应优化在分布式环境中。给定一组元启发式算法，DAMS的目标是协调它们在分布式节点上的本地执行，以优化分布式系统的全局性能。DAMS基于三层架构，允许节点分布式地决定要传递的本地信息，以及在优化过程进行时应用的元启发式。DAMS的自适应功能首先在一个非常一般的设置。一个特定的DAMS称为SBM，然后描述和分析，从并行和自适应的角度来看。SBM是一个简单的，但有效的，自适应分布式算法，利用组件允许节点选择最好的本地观察到的性能的元启发式，和一个探索组件允许节点检测元启发式的实际最佳性能。BSM-DAMS的效率证明通过实验和与其他自适应策略（顺序和分布式）的比较。
英文摘要：
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).
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