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Related Experiment Video

Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software

Jie Feng¹, Mingdong He¹, Lei Jin²

¹Guangdong Power Grid Co., Ltd., Guangzhou 510699, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

PFTuner optimizes software configurations by balancing multiple objectives like energy and reliability. This framework efficiently adapts to diverse systems, achieving superior performance and search efficiency compared to existing methods.

Keywords:

Bayesian Optimization configurable software systems multi-objective optimization parameters tuning

Related Experiment Videos

Last Updated: May 28, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Computer Science
Software Engineering
Optimization

Background:

Software systems have numerous parameters, making manual configuration difficult due to complex dependencies.
Existing automatic tuning often focuses on single objectives, neglecting crucial factors like energy consumption and reliability.
Multi-objective tuning faces challenges in handling conflicting goals and adapting to varied software configurations.

Purpose of the Study:

To introduce PFTuner, an efficient and adaptive multi-objective configuration tuning framework.
To address limitations in existing methods, including handling conflicting objectives and heterogeneous configuration spaces.
To improve the balance between search effectiveness and efficiency in software configuration tuning.

Main Methods:

PFTuner employs three modules: Configuration Generator, Configuration Evaluator, and Sample Collector, operating iteratively.
A novel multi-objective optimization algorithm is designed to model heterogeneous configuration spaces.
The framework balances optimization quality with search efficiency for adaptive tuning.

Main Results:

PFTuner was evaluated on eight software-workload scenarios, outperforming state-of-the-art baselines.
The framework consistently achieved higher-quality Pareto fronts, indicating better multi-objective optimization.
PFTuner demonstrated superior search efficiency and strong adaptability across diverse software systems and workloads.

Conclusions:

PFTuner offers an effective solution for multi-objective software configuration tuning.
The framework's adaptability and efficiency make it suitable for diverse software systems and workloads.
PFTuner advances the field by providing a balanced approach to optimization quality and search efficiency.