With the increasing volume of data and diverse user requirements, the cloud-edge-end architecture must adapt flexibly to address these challenges. However, many existing studies overlook the complexities of heterogeneous networks and task dependencies, leading to a mismatch between computational models and real-world applications. This paper addresses these issues by considering heterogeneity, diversity, and dependencies. We first establish a heterogeneous computing environment, and model the complex task dependencies using a directed acyclic graph (DAG). We then propose an improved evolutionary algorithm for task offloading and resource allocation. Experimental results show that our method enhances resource utilization and overall system performance in heterogeneous environments.