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Open Access

ARTICLE

An Enhanced Genetic Algorithm via an Innovative Elite Retention Strategy for Task Offloading in MEC Scenarios

Chengyu Hou1,2, Wenzao Li2, Hanyun Li3, Kui Liu1, Zhuoning Zhao1, Hongping Shu1,*
1 School of Software Engineering, Chengdu University of Information Technology, Chengdu, China
2 School of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
3 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
* Corresponding Author: Hongping Shu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081575

Received 07 March 2026; Accepted 12 May 2026; Published online 02 June 2026

Abstract

The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks, this paper proposes a task offloading strategy based on a refined elite mechanism in a GA. The algorithm introduces multi-point variation in both crossover and mutation operations to enhance population diversity, avoid local optima, and accelerate convergence. This design leverages the GA’s strength in multi-objective optimization, which outperforms other bionic heuristic algorithms that excel in single domains. Comparative experiments with GA, ant colony optimization, Deep Q-Network, Greedy algorithms, simulated annealing algorithm and particle swarm optimization, show that the proposed algorithm improves convergence speed by 35%, reduces task completion time by 6%, and optimizes energy consumption by approximately 18%.

Keywords

Task offloading; genetic algorithm; bandwidth constraint; mobile edge computing

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