An Evolutionary Multi-Task Optimization Algorithm Based on Affine Transformation in Edge Computing

In the domain of edge computing, one of the most significant challenges is the efficient allocation and scheduling of resources for multiple concurrent tasks given the constraints of limited computational resources. To address the resource optimization and scheduling challenges in edge computing, this paper introduces an Auxiliary Affine Transformation (AAT) algorithm designed to enhance cross-task alignment within the Evolutionary Multi-task Optimization (EMTO) framework. Specifically, AAT employs an iterative process where training samples are redistributed via affine transformation in each iteration. The algorithm dynamically establishes and optimizes task correspondences based on updated mapping results. By identifying the optimal alignment from existing transformations and iteratively refining the transformation matrix, AAT maximizes the similarity between heterogeneous tasks, thereby improving knowledge transfer efficiency in multi-task EMTO applications. Integrating AAT with EMTO addresses practical challenges in multi-task resource optimization and scheduling in edge computing environments. Experimental results show that the proposed method not only significantly enhances computational efficiency in task scheduling and optimization but also surpasses traditional methods in resource utilization and overall optimization effectiveness. In summary, AAT provides a more effective solution for resource optimization and task scheduling in edge computing while reinforcing the EMTO framework.