An Efficient Task Offloading Approach based on Multi-objective Evolutionary Algorithm in End-Edge-Cloud Collaborative Architecture

Zengzeng Zhang

doi:10.54691/z9jxrp40

Authors

Zengzeng Zhang

DOI:

https://doi.org/10.54691/z9jxrp40

Keywords:

Abstract

The dynamic and distributed nature of the "End-Edge-Cloud" collaborative architecture increases the uncertainty of task offloading decisions, leading to higher computational latency and energy consumption. This paper proposes the Improved Puma Optimization Algorithm (IPOA) to address the multi-objective optimization problem in the "End-Edge-Cloud" collaborative architecture. The IPOA enhances the traditional Puma Optimization Algorithm (POA) by introducing an adaptive solution acceptance strategy driven by charging dynamics and the Levy flight mechanism. Additionally, a multi-objective optimization model considering both latency and energy consumption is established. The IPOA effectively solves the multi-objective optimization problem. Experimental results demonstrate that, compared to various benchmark algorithms (such as POA,GWO, ABC, KOA, DE, and GA), the proposed algorithm reduces latency by more than 14% and energy consumption by more than 31%.

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