Task Scheduling Optimization in Cloud-Edge Collaborative Architecture via a Multi-Strategy Artificial Lemming Algorithm

In the cloud computing environment, various heterogeneous architectures have emerged, and the cloud-edge collaborative task scheduling architecture has come into being under this background. However, the complexity of cloud-edge heterogeneous architecture significantly restricts the improvement of scheduling performance. Therefore, researchers propose solving this problem by leveraging intelligent optimization algorithms. The Artificial Lemming Algorithm has received extensive attention due to its strong robustness. However, when dealing with the problem of cloud-edge collaborative task scheduling, there are still some drawbacks, such as long system response time and unstable scheduling performance. In response to the above problems, this paper proposes a multi-strategy artificial lemming algorithm. Specifically, by coordinating high-order Chebyshev polynomials with chaotic mapping to enhance the richness of the initial population, the scheduling response time is indirectly shortened. Secondly, the Adaptive Spatial Search Mechanism is introduced to make up for the deficiencies in the exploration stage, enhance the algorithm’s exploration ability, and thereby improve the optimization effect of scheduling satisfaction. Furthermore, the Bernstein-Guided Correction Strategy is introduced to enhance the exploitation capability of the algorithm to improve the stability of cloud-edge scheduling. The experimental results demonstrate that compared with the baseline algorithms, the proposed MALA reduces the total scheduling cost by at least 3% across cloud-edge collaborative resource scheduling problems of different scales.