Abstract: Cloud computing service composition integrates services, distributed and diverse by nature, into an integrated entity that can meet a user's requirement with better effectiveness. However, some obstacles regarding high latency and suboptimal Quality of Service (QoS) still exist in a dynamic multi-cloud environment. This study addresses the limitations of traditional optimization algorithms in service composition, specifically the premature convergence and lack of population diversity in the Moth-Flame Optimization (MFO) algorithm. We propose the modified MFO algorithm with a new mechanism called Stagnation Finding and Replacement (SFR) to enhance the diversity of the population. It finds the static solutions based on a distance metric from globally optimal representative solutions and replaces them. MFO-SFR drastically improved all QoS metrics, such as response time, delay, and service stability. Empirical evaluations prove that MFO-SFR outperforms the baseline methods of multi-cloud service composition. It provides a computationally efficient and adaptive solution to cloud service composition problems, ensuring better resource utilization and higher user satisfaction in dynamic multi-cloud environments.

Yeling YANG and Miao SONG, “Modified Moth-Flame Optimization Algorithm for Service Composition in Cloud Computing Environments” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160188

@article{YANG2025,
title = {Modified Moth-Flame Optimization Algorithm for Service Composition in Cloud Computing Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160188},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160188},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Yeling YANG and Miao SONG}
}