多Agent并行遗传算法在地震勘探属性优化中的应用  

Seismic Exploration Attribute Optimization Based on Multi-Agent Parallel Genetic Algorithm

作　　者：刘其成[1,2] 郑纬民[1] 

机构地区：[1]清华大学计算机科学与技术系,北京100084 [2]烟台大学计算机学院,烟台264005

出　　处：《计算机科学》2010年第4期234-237,共4页Computer Science

基　　金：973国家重点基础研究规划计划(2007CB310903);国家自然科学基金(60703055;60673144)资助

摘　　要：研究了基于多Agent的并行遗传算法,并将其应用于石油勘探的属性优化。针对常规遗传算法的不足,采用Agent构建多Agent系统实现了基于粗粒度的并行遗传算法,该算法能从进化环境中获取表征当前进化状态的有用信息,智能地监控调度GA的进化操作,在避免早熟的同时加快全局寻优,提高遗传算法搜索的效率,同时具有通讯开销小的特点。将该方法用于地震勘探属性优化,取得了良好的效果。The method of design and implementation for parallel genetic algorithm is based on thick grain, with the multi-Agent combined with genetic evolution technology, to optimize the seismic attribution selection. A multi-Agent system includes two kinds of Agents：N-Agent and M-Agent, they can exchange the useful information which can represent the current situation of evolution. The methods are benefit to improve the performance of parallel genetic algorithm, and to raise the searching efficiency of the parallel genetic algorithm. The projecting feature in the parallel module is less communicating overhead. It was used in the optimization of oil exploration attribute selection and the result is good.

关 键 词：AGENT 属性优化 遗传算法 并行 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]