基于多Agent协同构建分布式并行遗传算法

针对并行遗传算法中计算资源的分配问题 ,提出了分布式并行遗传算法结构。它由若干计算节点组成 ,每个节点包含若干运行子遗传算法的计算单元。节点的计算能力依照一定的并行模式映射到单元 ;各子算法则根据一定的拓扑结构进行个体交换。从多 Agent系统的观点看 ,计算单元是独立的 Agent,其并行运行涉及计算资源的分配 ,体现了算法对它们的协调 ;个体的迁移体现了它们之间的协作。并且分析了由两个单元构成的算法在不同并行模式和不同个体迁移因子下的性能。

Distributed parallel genetic algorithms based on multi-agent cooperation

An architecture of a distributed parallel genetic algorithm was developed to improve computing resource allocation in parallel genetic algorithms. The architecture was defined on a network consisting of several computing nodes each of which had several computing units. The algorithm mapped the physical computing nodes to logical computing units using certain parallel mode and carried through individual migrating between neighboring units. A system with only two units was used to analyze the performance of the architecture. Four parallel modes, the serial, the simple parallel, the quasi -parallel and the migrating parallel, as well as the individual migrating fractions were introduced. The experiments on the search for the global maximum of the Schaffer function show that the quasi -parallel mode is better than the simple parallel mode or the serial mode when there is only one node, while the migrating parallel mode is better than all the other modes for certain range of computing power re -mapping fraction. Heuristic arguments were also provided for the understanding of these observations.