Adaptive Honey Badger Optimization with Bernoulli Chaotic Mapping and Decreasing Neighborhood for Efficient Cloud Task Scheduling

One of the most important challenges in optimizing task scheduling in cloud computing is the dynamic nature of resources, the heterogeneity of tasks, and conflicting optimization criteria, such as minimizing task completion period, optimizing resource utilization, and shortening task migration time. Meta-heuristics such as the Honey Badger Algorithm (MBA) often converge on suboptimal solutions and may not strike the perfect balance between exploitation and exploration in task scheduling optimization. To address the problems associated with traditional HBA and similar algorithms, this research introduces a novel optimization technique called the Multi-strategy Honey Badger Algorithm (MHBA). The proposed MHBA integrates three optimization strategies: horizontal crossing coupled with adaptation, an optimum decreasing neighborhood, and a Bernoulli shift scheme. The MHBA is simulated using CloudSim and compared with other advanced techniques. The experimental findings confirm MHBA's efficacy in reducing makespan by up to 25.6%, increasing resource utilization by up to 16.7%, and decreasing migration time by up to 27.5% when applied to the HPC2N dataset. The same was evident in the NASA dataset, where MHBA achieved reductions in makespan of up to 25.9%, improvements in resource utilization of up to 21.6%, and reductions in migration time of up to 24.1%.

@article{XING2026,
  title     = {Adaptive Honey Badger Optimization with Bernoulli Chaotic Mapping and Decreasing Neighborhood for Efficient Cloud Task Scheduling},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Yanfang XING},
  doi       = {10.14569/IJACSA.2026.0170673},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170673}
}