ECOA: An Enhanced Chimp Optimization Algorithm for Cloud Task Scheduling

Effective scheduling of tasks is a key concern in cloud computing because it considerably affects system functionality, resource usage, and execution efficiency. The present study proposes an Enhanced Chimp Optimization Algorithm (ECOA) to address such problems by overcoming the disadvantages of traditional scheduling methods. The proposed ECOA combines three innovative components: 1) the highly disruptive polynomial mutation enhances population diversity, 2) the Spearman rank correlation coefficient promotes the refinement of inferior solutions, and 3) the beetle antennae operator facilitates more efficient local exploitation. These changes significantly enhance the equilibrium between exploration and exploitation, decrease the chance of premature convergence, and are a better solution. Extensive experiments on benchmark datasets prove that ECOA outperforms traditional algorithms concerning makespan, imbalance degree, and resource utilization. The obtained results confirm that the proposed ECOA has excellent potential for better performance in task scheduling in dynamic and large-scale cloud environments, as it represents a promising optimization solution for complex problems in cloud computing.

@article{WANG2025,
  title     = {ECOA: An Enhanced Chimp Optimization Algorithm for Cloud Task Scheduling},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yue WANG},
  doi       = {10.14569/IJACSA.2025.0160564},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160564}
}