摘要
体系具有复杂性、巨大性和交互性等特征,体系需求的获取和分析则面临方案的不确定性和方案空间庞大等难题,利用启发式搜索算法可以求解规模较大的能力方案,但效率较低。在分析能力方案构成描述中定性、定量等要素特点的基础上,针对不同能力方案求解时算法执行效率差异较大的特征,提出面向智能Agent的三维概率选择矩阵算法,利用智能Agent自学习存储方式,将多种启发式优化算法求解不同类型的能力方案时的效率存储起来,建立三维选择矩阵,求解时动态选择效率高的算法,提高算法整体执行效率。在求解某体系能力规划方案时体现了此算法根据问题动态选择算法的优势。
Complexity,tremendous and interactivity are main characters of system of systems（SoSs）.A requirement analysis of SoSs faces some questions,such as great uncertainties and huge space of solutions.The heuristic algorithm could settle some NP-hard problems,but efficiency of heuristic algorithm is lower when the complexity of the problem becomes higher.Capabilities solutions of SoSs have their own traits.Each heuristic algorithm is expert in computing different kinds of capabilities solution.This article proposes an algorithm based on intelligent agent by choosing a 3-dimension probabilities matrix.Using the self-learning of agent,the method stores the history experience which is applied to solve such kind of SoSs requirement solutions.The history experience of Agents could be stored in the 3-dimension matrix.When dealing with huge complex SoSs requirement solutions,the Agent can choose the most efficient algorithm to solve the proper problem.This is illustrated with a case study of military SoSs,and the result shows greatly robust and efficient advantages under this context.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第6期1220-1224,共5页
Systems Engineering and Electronics
基金
国家自然科学基金(70901074)资助课题
关键词
基于能力规划
体系需求
优化算法
AGENT
capability based planning
system of systems（SoSs）
requirement optimal algorithm
Agent
