[1]FANG Haotian,TIAN Le,GUO Maozu.Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(6):1573-1583.[doi:10.11992/tis.202312042]
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Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1573-1583 Column: 人工智能院长论坛 Public date: 2024-12-05
Title:
Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm
Author(s):
FANG Haotian;  TIAN Le;  GUO Maozu
Beijing Key Laboratory of Intelligent Processing of Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 102600, China
Keywords:
moving edge computing; calculating offloading; artificial fish swarm algorithm; artificial colony algorithm; self-similar queuing model; gaussian attenuation function; particle swarm optimization; inertia weight factor
CLC:
TP393
DOI:
10.11992/tis.202312042
Abstract:
In the network architecture of mobile edge computing, an offloading decision controller was introduced to balance the reduction of energy consumption and delay. This controller obtains the optimal offloading decision through an offloading decision optimization algorithm. A new ABC–FS algorithm was proposed by combining the artificial bee colony (ABC) algorithm and the artificial fish swarm (FS) algorithm. Additionally, a Gaussian decay function was introduced to transition the algorithm parameters from static to dynamic, and the inertia weight factor of the improved particle swarm optimization algorithm was incorporated, creating a multi-population hybrid intelligent optimization algorithm. Finally, an objective function that jointly optimizes delay and energy consumption was designed, and simulation experiments were conducted using Poisson probability. Simulation results show that the proposed offloading strategy optimization algorithm achieves faster convergence speed compared to several benchmark methods and effectively balances the reduction of total task offloading delay and total energy consumption in multi-access edge computing scenarios.
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