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

• URL: http://arxiv.org/abs/2609.07389v1
• Date: Mon, 07 Sep 2026 12:03:10 GMT
• Title: A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization
• Abstract summary: We build surrogate models based on regression techniques to approximate the outputs of analysis tools at significantly lower computational cost.<n>Our experimental results suggest that surrogate models provide savings of up to $30%$ in computational time.<n>Overall, surrogate models constitute a promising approach for scaling multi-objective architecture optimization to larger spaces and complex architectural models.
• Score: 0.9430947207126281
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Deep Unfolding: Recent Developments, Theory, and Design Guidelines [99.63555420898554]
  This article provides a tutorial-style overview of deep unfolding, a framework that transforms optimization algorithms into structured, trainable ML architectures.<n>We review the foundations of optimization for inference and for learning, introduce four representative design paradigms for deep unfolding, and discuss the distinctive training schemes that arise from their iterative nature.
  arXiv  Detail & Related papers  (2025-12-03T13:16:35Z)
• Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem [0.0]
  This study presents a methodology to treat performance-based seismic design as an inverse engineering problem.<n>By implementing explainable machine learning models, this methodology directly maps design variables and performance metrics.<n>The developed methodology is then applied to two different inventories of steel and concrete moment frames in Los Angeles and Charleston.
  arXiv  Detail & Related papers  (2025-08-01T03:08:19Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization [4.038368925548051]
  Vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment. Traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance. Recently, AI models have been used to reduce the computational cost of FEA.
  arXiv  Detail & Related papers  (2024-10-03T23:54:03Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies [0.8646443773218541]
  This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.
  arXiv  Detail & Related papers  (2023-12-06T18:20:46Z)
• On the Role of Search Budgets in Model-Based Software Refactoring Optimization [4.065502917666599]
  Multi-objective evolutionary algorithms help designers identify trade-offs among desired non-functional properties.<n>This work examines the impact of implementing a search budget to limit the search for design alternatives.
  arXiv  Detail & Related papers  (2023-08-29T10:01:29Z)
• Introducing Interactions in Multi-Objective Optimization of Software Architectures [3.483484684588142]
  This study investigates the impact of designer interactions on software architecture optimization.<n>By directing the search towards regions of interest, the interaction uncovers architectures that remain unexplored in the fully automated process.
  arXiv  Detail & Related papers  (2023-08-29T07:49:46Z)
• A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences [55.25331349436895]
  Deep generative models have emerged as a popular machine learning-based approach for inverse problems in the life sciences. These problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution.
  arXiv  Detail & Related papers  (2022-10-19T19:04:45Z)
• Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components [71.03032589756434]
  We investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models. We characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.
  arXiv  Detail & Related papers  (2021-06-16T17:12:26Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.