Modeling and convergence analysis of distributed coevolutionary algorithms
Modeling and convergence analysis of distributed coevolutionary algorithms
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DOI：
10.1109/tsmcb.2003.817095
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发表时间：
2004-04
期刊：
影响因子：
--
通讯作者：
R. Subbu;A. Sanderson
中科院分区：
文献类型：
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作者：
R. Subbu;A. Sanderson
为求解变量在p个节点之间划分的优化问题的一类分布式协同进化算法的建模和收敛性分析提供了理论基础。在p个节点中的每个节点处的进化算法基于其自身的主变量集执行局部进化搜索，并且在该阶段期间每个节点处的次变量集被钳制。节点之间不频繁的相互通信更新每个节点的次要变量。本地搜索和相互通信阶段交替进行，导致p个节点的合作搜索。首先，我们指定了一类集中式进化算法的理论基础，在可行空间上的采样分布的建设和发展。接下来，这个基础上扩展到开发一类分布式协同进化算法的模型。收敛性和收敛速度分析追求的基本类的目标函数。我们的理论研究表明，对于某些单峰和多峰的目标，我们可以预期这些算法收敛的几何速度。分布式协同进化算法是最感兴趣的角度来看，他们的性能优势相比，集中式算法，当他们在网络环境中执行显着的本地访问和节点间的通信延迟。因此，这些算法的相对性能进行评估，在一个分布式环境中的网络行为的现实参数。
A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyses are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.