Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks

Li, Xujie; Zang, Zhennan; Shen, Fei; Sun, Ying

doi:10.1007/s11036-020-01593-5

Cited by 26 publications

(9 citation statements)

References 32 publications

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“…, T n } of a job J. Baccarelli et al [30] • eGA De Maio and Kimovski [31] • eNSGA2 Kabirzadeh et al [32] • Hyb. Wang et al [33] • • eGA Abbasi et al [34] • NSGA2 Aburukba et al [35] • GA Ahmed et al [36] • GA Ali et al [37] • eNSGA2 Binh et al [38,39] • GA Hou et al [40] • eGA Li et al [41] • eGA Li et al [42] • Hyb. Liu et al [43] • eGA Ma et al [44] • eGA Mekki et al [45] • NSGA2 Nikoui et al [46] • GA Ren et al [47] • Hyb.…”

Section: Task Schedulingmentioning

confidence: 99%

“…Li et al [42] address the problem of task offloading and its partition into independent subtasks. They propose a hybrid algorithm that combines the characteristics of a GA with a beetle antennae search (BAS).…”

Section: Scheduling In Vehicular Domainsmentioning

confidence: 99%

“…In these cases, the entire optimization execution flow is redesigned, integrating and interleaving the tasks of each of the heuristics to be combined. For example, Li et al [42] include an additional intermediate task to the classic structure of a GA, the evolution operation of a BAS to improve the solutions obtained from selection, crossing, and mutation. Similarly, Yadav et al [83] use a GA to control the evolution of the population but integrating the particle motion operations of a PSO to accelerate the evolution of solutions.…”

Section: Hybrid Gamentioning

confidence: 99%

See 2 more Smart Citations

Genetic-based optimization in Fog Computing: current trends and research opportunities

Guerrero,

Lera,

Juiz

2021

Preprint

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Fog Computing is a new computational paradigm that emerges from the need of reducing the network usage and latency in Internet of Things (IoT). Fog can be understood as a continuum between the Cloud layer and the IoT users that allows to execute applications or store/process data in the networks devices of the infrastructure. This paper aims to review current trends in the use of Genetic Algorithm for the optimization of resource management in Fog architecture. Related papers are classified concerning the optimization scope and the genetic. Finally, future research lines are also presented.

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Section: Task Schedulingmentioning

confidence: 99%

Section: Scheduling In Vehicular Domainsmentioning

confidence: 99%

Section: Hybrid Gamentioning

confidence: 99%

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Genetic-based optimization in Fog Computing: current trends and research opportunities

Guerrero,

Lera,

Juiz

2021

Preprint

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“…e core code in BAS is very short, only four lines, and it is easy to be implement in the computer. For BA and artificial bee colony (ABC), BAS owns a higher efficiency and lower complexity [26,27] and is used in many fields [28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A PID Tuning Strategy Based on a Variable Weight Beetle Antennae Search Algorithm for Hydraulic Systems

Qiao

Fan

2021

Advances in Materials Science and Engineering

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To select reasonable PID controller parameters and improve control performances of hydraulic systems, a variable weight beetle antenna search algorithm is proposed for PID tuning in the hydraulic system. The beetle antennae search algorithm is inspired by the beetle preying habit depending on symmetry antennae on the head. The proposed algorithm added the exponential equation mechanism strategy in the basic algorithm to further improve the searching performance, the convergence speed, and the optimization accuracy and obtain new iteration and an updating method in the global searching and local searching stages. In the PID tuning process, advantages of less parameters and fast iteration are realized in the PID tuning process. In this paper, different dimension functions were tested, and results calculated by the proposed algorithm were compared with other famous algorithms, and the numerical analysis was carried out, including the iteration, the box-plot, and the searching path, which comprehensively showed the searching balance in the proposed algorithm. Finally, the reasonable PID controller parameters are found by using the proposed method, and the tuned PID controller is introduced into the hydraulic system for control, and the time-domain response characteristics and frequency response characteristics are given. The results show that the proposed PID tuning method has good PID parameter tuning ability, and the tuned PID has a good control ability, which makes the hydraulic system achieve the desired effect.

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“…Because such NLP problems are widely approached by heuristic, a binary beetle antennae search algorithm (BBAS), which is introduced in [8], is employed to provide a solution to the BTPSCC problem. Generally, the beetle antennae search is a memetic meta-heuristic optimization algorithm and has been employed broadly in various scientific fields in the last few years (see [9][10][11][12][13][14][15][16][17]). For example, inline with beetle antennae search (BAS) and enhanced contract net protocol, an assignment framework for fog computing networks is presented in [10].…”

Section: Introductionmentioning

confidence: 99%

Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

Katsikis¹,

Mourtas²

2021

J. Model. Optim.

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The tangency portfolio, also known as the market portfolio, is the most efficient portfolio and arises from the intercept point of the Capital Market Line (CML) and the efficient frontier. In this paper, a binary optimal tangency portfolio under cardinality constraint (BOTPCC) problem is defined and studied as a nonlinear programming (NLP) problem. Because such NLP problems are widely approached by heuristic, a binary beetle antennae search algorithm is employed to provide a solution to the BTPSCC problem. Our method proved to be a magnificent substitute to other evolutionary algorithms in real-world datasets, based on numerical applications and computer simulations.

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Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks

Cited by 26 publications

References 32 publications

Genetic-based optimization in Fog Computing: current trends and research opportunities

Genetic-based optimization in Fog Computing: current trends and research opportunities

A PID Tuning Strategy Based on a Variable Weight Beetle Antennae Search Algorithm for Hydraulic Systems

Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

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