A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization

Han, Dong‐Guk; Jing, Wang; Tang, Canren; Weng, Tien‐Hsiung; Li, Kuan‐Ching; Dobre, Ciprian

doi:10.1002/dac.4924

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…Evolutionary algorithms are often used to solve complex NP problems, mainly including genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. 26 Fan et al 27 proposed a self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies. Abido 28 developed the strength Pareto evolutionary algorithm (SPEA) and successfully applied it to the environmental electric power dispatch problem.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

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An evolutive framework for server placement optimization to digital twin networks

Xiao

Han

Weng

et al. 2023

Int J Communication

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SummaryDigital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real‐time data and requires interaction with the physical network in real‐time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP‐hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline‐learning‐based approach is used to reduce the search space, and a self‐examining guided local search method is proposed to improve the search efficiency. Additionally, a look‐up table‐based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.

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Section: Evolutionary Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An evolutive framework for server placement optimization to digital twin networks

Xiao

Han

Weng

et al. 2023

Int J Communication

Self Cite

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“…Huang et al proposed a three-dimensional localization algorithm for WSNs based on improved A * and DV-Hop algorithms [32]. Han et al proposed a multitarget vector hopping localization algorithm based on differential evolution quantum particle swarm optimization [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

2022

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Node localization information plays an important role in wireless sensor networks (WSNs). To solve the problem of low localization accuracy of distance vector hop (DV-Hop) localization algorithm in wireless sensor networks, an improved localization algorithm called MAOADV-Hop based on the modified Archimedes optimization algorithm (MAOA) and DV-Hop is proposed, which can achieve the balance between the localization speed and the localization precision. Firstly, tent chaotic mapping and particle swarm optimization (PSO) algorithm are introduced into Archimedes optimization algorithm to improve the initial population diversity and change the update rules of density and volume, which improve the global convergence ability and convergence speed of the algorithm. Secondly, the MAOA is used to replace the least square part of the DV-Hop localization algorithm to improve the localization accuracy of the algorithm. Finally, MAOADV-Hop is verified through four different network environments and compared with DE_DV-Hop, BOA_DV-Hop, and DV-Hop. The simulation results show that the localization speed of the proposed approach is faster than that of DE_DV-Hop and BOA_DV-Hop, and the localization error is less than that of DV-Hop, DE_DV-Hop, and BOA_DV-Hop.

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Green energy‐efficient computing solutions in Internet of Things communications

Souri

Piuri

Shojafar

et al. 2021

Int J Communication

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A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization

Cited by 9 publications

References 43 publications

An evolutive framework for server placement optimization to digital twin networks

An evolutive framework for server placement optimization to digital twin networks

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

Green energy‐efficient computing solutions in Internet of Things communications

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