目前基于规则的软件体系结构（Software Architecture，简记为SA）层性能优化方法大多未充分考虑优化过程中规则的使用次数和使用顺序的不确定性，导致了搜索空间受限而难以获取更优的性能改进方案。针对这一问题并以最小化系统响应时间为优化目标，文中首先定义一种基于规则的SA层性能优化模型RPOM，以将SA层性能优化抽象为求解最优规则序列的数学问题；然后设计一种支持SA层性能改进规则序列执行的框架RSEF；进一步提出一种采用约束检查、修复及统计学习机制的演化求解算法EA4PO；最后以Web应用为案例与已有方法进行实验对比。结果表明：（1）本文方法较已有方法可以获取更短的系统响应时间；（2）EA4PO所引入的统计学习机制可显著提高演化求解算法的收敛速度和解质量。%In the existing rule-based performance optimization approaches at software architecture (SA)level,it has not been fully concerned that the count and the order of each rule usage are uncertain in the optimization process.As a re-sult,the search space for performance improvement is limited and the better solutions are hard to find.Aiming to this prob-lem and taking the system response time minimum as the optimization objective,firstly,a model called RPOM is defined to abstract rule-based software performance optimization at SA level as the mathematical problem for solving the optimal rule sequence.Secondly,a framework named RSEF is designed to support the execution of a rule sequence.Furthermore,an evo-lutionary algorithm named EA4PO is proposed to find the optimal performance improvement solution by introducing statisti-cal learning,constraint checking and repairing.Finally,a web application is taken as a case in the experiments for comparing with the existing methods.The experimental results indicate that the shorter system response time can be obtained and the statistical learning can obviously improve the convergence rate and the solution quality in our approach.
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