Architecture-based software performance optimisation can help to find potential performance problems and mitigate their negative effects at an early stage. To automate this optimisation process, rule-based and metaheuristic-based performance optimisation methods have been proposed. However, existing rule-based methods explore a limited search space, potentially excluding optimal or near-optimal solutions. Most of current metaheuristic-based methods ignore existing practical knowledge of performance improvement, and lead to solutions that are not easily explicable to humans. To address these problems, we propose a novel approach for performance optimisation at the software architecture level named Multiobjective performance Optimisation based on Randomised search rulEs (MORE). First, we design randomised search rules (MORE-R) to provide explanation without parameters while benefiting from existing practical knowledge of performance improvement. Second, based on all possible composite applications of MORE-R, an explicable multi-objective optimisation problem (MORE-P) is defined to enlarge search space and enable solutions explicable to architectural stakeholder. Third, a multi-objective evolutionary algorithm (MORE-EA) with an introduced do-nothing rule, innovative encoding and repair mechanism is designed to effectively solve MORE-P. The experiments show that MORE is able to achieve more explicable and higher quality solutions than two state-of-the-art techniques. They also demonstrate the benefits of integrating search-based software engineering approaches with practical knowledge.

基于体系结构的软件性能优化可以帮助发现潜在的性能问题，并在早期减轻其负面影响。为了使该优化过程自动化，已经提出了基于规则和基于元启发式的性能优化方法。但是，现有的基于规则的方法探索了有限的搜索空间，可能排除了最佳或接近最佳的解决方案。当前大多数基于元启发式的方法都忽略了现有的性能改进实践知识，并导致了不易为人类使用的解决方案。为了解决这些问题，我们提出了一种基于随机搜索规则（MORE）的软件体系结构级性能优化的新方法，称为多目标性能优化。第一的，我们设计了随机搜索规则（MORE-R），以便在不使用参数的情况下提供解释，同时受益于性能改进的现有实践知识。其次，基于MORE-R的所有可能的复合应用程序，定义了一个可解释的多目标优化问题（MORE-P），以扩大搜索空间并启用可适用于体系结构涉众的解决方案。第三，设计了多目标进化算法（MORE-EA），引入了无为规则，创新的编码和修复机制，有效地解决了MORE-P问题。实验表明，与两种最先进的技术相比，MORE能够获得更多可解释的质量更高的解决方案。他们还展示了将基于搜索的软件工程方法与实践知识相集成的好处。