Task Offloading Strategy of Vehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm

Shen, Xianhao; Chang, Zhaozhan; Xie, Xiaolan; Niu, Shaohua

doi:10.3390/app12189308

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…. , 𝑛 on the location 𝑥 of the nest to the interval [0,1] by the following formula (Shen et al, 2022):…”

Section: Chaos Initializationmentioning

confidence: 99%

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Ren,

Meng,

Liu

et al. 2024

10.5267/j.ijiec

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This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

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“…. , 𝑛 on the location 𝑥 of the nest to the interval [0,1] by the following formula (Shen et al, 2022):…”

Section: Chaos Initializationmentioning

confidence: 99%

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Ren,

Meng,

Liu

et al. 2024

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Shen et al designed a collaborative computing framework for task offloading, integrating an improved bald eagle search optimization algorithm. This framework aims to optimize latency and energy consumption, effectively reducing the total cost of task offloading in vehicular networks [17] without considering the mobility and offloading rates of vehicles. Deng et al investigated an edge collaboration task offloading and splitting strategy, minimizing the total cost of delay and energy consumption [18].…”

Section: Introductionmentioning

confidence: 99%

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

Gao,

Luo,

Zhan

et al. 2024

Symmetry

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In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.

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Multi-threshold segmentation of grayscale and color images based on Kapur entropy by bald eagle search optimization algorithm with horizontal crossover and vertical crossover

Yue

Zhu

2023

Soft Comput

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Task Offloading Strategy of Vehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm

Cited by 9 publications

References 22 publications

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

Multi-threshold segmentation of grayscale and color images based on Kapur entropy by bald eagle search optimization algorithm with horizontal crossover and vertical crossover

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