Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments

The proliferation of data-intensive mobile applications has necessitated efficient computation offloading techniques to mitigate resource constraints in mobile devices (MDs). Existing approaches often fail to address multi-objective optimization challenges effectively. This paper proposes an Enhanced Adaptive Cat Hunt Optimization (EACHO) algorithm, designed to optimize energy consumption (EC), delay, and resource utilization in heterogeneous Mobile Edge Computing (MEC) environments. The model leverages Directed Acyclic Graphs (DAGs) for task representation and adaptive parameters for real-time decision-making. Experimental results demonstrate that EACHO achieves significant reductions in delay (0.0172 seconds), EC (0.251 × 10−3 J), and cost (0.387) compared to state-of-the-art methods. These findings highlight the robustness and scalability of EACHO for diverse MEC scenarios.