基于一种自适应并行遗传算法的网格资源选择策略

网格是在某一单一时间，将网络中众多的计算机资源集中起来共同处理某个单一问题的.而如何有效地从众多的资源中选出多个较优秀的资源是一个NP问题.该文提出一种新的自适应的并行遗传算法（NAPGA），并对网格资源的选择策略在C+MPI平台上进行了并行模拟.结果表明，该算法不仅有效地避免了过早收敛的现象，而且取得了比改进型的并行遗传算法(NIPGA)更优的搜索结果.最后对遗传算法的搜索和收敛规律进行了一些讨论.

Grid Resource Selection Strategy Based on an Adaptive Parallel Genetic Algorithm

With a large amount of computer resources becoming available in the network, grid can been used to handle a single problem simultaneously. Selecting more than one outstanding resource from numerous resources is an NP problem. In this paper, a new adaptive parallel genetic algorithm (NAPGA) is proposed, with which parallel model simulation of the grid resources selection strategy on C+MPI platform is made. The result indicates that the algorithm can effectively solve the problem of premature convergence, and produce results that are better than a new improved parallel genetic algorithm (NIPGA). Searching and converging disciplinarian of genetic algorithm are discussed.