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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

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Published on: January 20, 2022

Cloud service composition optimization based on service association impact and improved NSGA-II algorithm.

Chong Zhang¹, Longge Wang¹, Ketai He²

¹School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

Scientific Reports

|July 17, 2025

Summary

This summary is machine-generated.

This study optimizes cloud manufacturing service composition by introducing a new model and enhanced NSGA-II algorithm. The approach effectively balances service quality, cost, and association impact for better performance.

Keywords:

Cloud manufacturing Improved NSGA-II algorithm Service association impact Service composition optimization

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Area of Science:

Cloud Manufacturing
Operations Research
Multi-objective Optimization

Background:

Service composition in cloud manufacturing is complex, balancing quality, cost, and association impact.
Existing methods often neglect the quantitative effect of service associations, leading to suboptimal outcomes.

Purpose of the Study:

To develop a novel three-objective optimization model for cloud manufacturing service composition.
To introduce a service association cost function to quantify impact.
To propose an enhanced NSGA-II algorithm for efficient model solving.

Main Methods:

Developed a three-objective optimization model incorporating service quality, cost, and service association effects.
Proposed an enhanced NSGA-II algorithm with specialized initialization, crossover, and elitism strategies.
Conducted experiments on benchmark problems and cloud service composition scenarios.

Main Results:

The enhanced NSGA-II algorithm demonstrated superior convergence, diversity, and robustness compared to conventional methods.
Experimental results validated the model's effectiveness in addressing conflicting objectives.
The approach showed practical applicability in real-world cloud manufacturing.

Conclusions:

The proposed optimization model and enhanced NSGA-II algorithm effectively address the complexities of cloud manufacturing service composition.
Accounting for service association impact is crucial for achieving optimal composition performance.
The developed method offers a robust and efficient solution for practical cloud manufacturing environments.