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<p>The rapid development of cloud computing technologies has increased the complexity of workflow scheduling problems. In heterogeneous cloud environments, resource diversity and dynamism present significant challenges for scheduling optimization. In this paper, a hybrid improved weak cooperation multi-objective dung beetle optimization scheduling algorithm (HCMDBOA) is proposed for the simultaneous optimization of Makespan and Cost. The algorithm combines dung beetle optimization (DBO) algorithm with non-dominated sorting genetic algorithm II (NSGA-II), employing a dual-population weak cooperation strategy to improve convergence and solution diversity. The population diversity is enhanced by introducing a good point set strategy and incorporating a modified sine algorithm (MSA) to balance global exploration and local exploitation. Additionally, an adaptive Gauss-Cauchy mutation operator is employed to enhance the effectiveness and adaptability in high-dimensional and dynamic scheduling environments. The experimental data suggest that HCMDBAO exhibits superior diversity and convergence of Pareto solution sets compared to the other seven methods.</p>

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A hybrid dung beetle optimization algorithm with improved weak cooperation for multi-objective workflow scheduling in heterogeneous cloud environments

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Fan Ding,
  • Lizhi Lv,
  • Meng Zhou,
  • Rui Zhang

摘要

The rapid development of cloud computing technologies has increased the complexity of workflow scheduling problems. In heterogeneous cloud environments, resource diversity and dynamism present significant challenges for scheduling optimization. In this paper, a hybrid improved weak cooperation multi-objective dung beetle optimization scheduling algorithm (HCMDBOA) is proposed for the simultaneous optimization of Makespan and Cost. The algorithm combines dung beetle optimization (DBO) algorithm with non-dominated sorting genetic algorithm II (NSGA-II), employing a dual-population weak cooperation strategy to improve convergence and solution diversity. The population diversity is enhanced by introducing a good point set strategy and incorporating a modified sine algorithm (MSA) to balance global exploration and local exploitation. Additionally, an adaptive Gauss-Cauchy mutation operator is employed to enhance the effectiveness and adaptability in high-dimensional and dynamic scheduling environments. The experimental data suggest that HCMDBAO exhibits superior diversity and convergence of Pareto solution sets compared to the other seven methods.