A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization

2026-09-07

AI Summary

本文提出一种基于代理模型的方法，利用回归技术近似分析工具输出，以降低计算成本并保持合理精度，从而有效解决复杂软件模型多目标架构优化中的效率问题。

Abstract

Software model optimization is a process that generates architecture alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms are commonly used to explore the search space and help designers identify trade-offs among competing non-functional properties (e.g., through a Pareto front). However, such algorithms face efficiency challenges in complex software models and large design spaces, since evaluating the fitness (i.e., the quality) of each architecture requires analysis tools that become computationally expensive when repeatedly invoked during the search process. In this paper, we explore the construction of surrogate models based on regression techniques to approximate the outputs of these analysis tools at significantly lower computational cost, while maintaining reasonable output accuracy. Our experimental results suggest that surrogate models provide savings of up to $30\%$ in computational time and maintain the Pareto front quality provided by evolutionary algorithms. Also, we observed some differences in the architectural models produced by our approach. Overall, surrogate models constitute a promising approach for scaling multi-objective architecture optimization to larger spaces and complex architectural models.

Problem

• multi-objective evolutionary algorithms
• software model optimization
• computational cost
• efficiency challenges
• analysis tools

Innovation

• surrogate models
• regression techniques
• multi-objective optimization
• computational efficiency
• Pareto front