A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks

Zhang, Xin; Zhang, Xiu

doi:10.1016/j.adhoc.2016.07.001

Cited by 43 publications

(19 citation statements)

References 25 publications

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“…Table 2 lists the correspondence between edges and nodes, as well as the weight of the edges. Our new proposed algorithm will be compared with Kruskal algorithm and BABC [40] algorithm. BABC uses the basic artificial bee colony algorithm to solve the minimum spanning tree.…”

Section: Methodsmentioning

confidence: 99%

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

Zhao

Zhang

2019

J Wireless Com Network

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In industrial Internet, many intelligent applications are implemented based on data collection and distribution. Data collection and data distribution in the wireless sensor networks are very important, where the node topology can be described by the spanning tree for obtaining an efficient transmission. Classical algorithms in graph theory such as the Kruskal algorithm or Prim algorithm can only find the minimum spanning tree (MST) in industrial wireless sensor networks. Swarm intelligence algorithm can obtain multiple solutions in one calculation. Multiple solutions are very helpful for improving the reliability of industrial wireless sensor networks. In this paper, we combine quantum computing with artificial bee colony and design a spanning tree construction algorithm for industrial wireless sensor networks. Quantum computations are introduced into the onlooker bees search. Food source replacement strategy is improved. Finally, the algorithm is simulated and evaluated. The results show that the new proposed algorithm can obtain more alternative solutions and has a better performance in search efficiency.

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Section: Methodsmentioning

confidence: 99%

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

Zhao

Zhang

2019

J Wireless Com Network

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“…But still, there is a gap in providing a minimum number of clusters to provide more enhanced VANET services. Similarly, recently proposed protocols like [27] and [28] are based on SI techniques, namely, dragonfly and grey wolf optimization algorithms. Individually, these protocols have performed better as compared to present techniques CLPSO, MOPSO [29], and ACO [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

et al. 2020

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In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.

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“…In [42], a binary ABC was used to solve the spanning tree construction problem. Compared to the traditional Kruskal algorithm, the binary ABC could find sub-optimal spanning trees.…”

Section: Related Workmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Xiao

Wang

et al. 2019

Mathematics

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Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

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A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks

Cited by 43 publications

References 25 publications

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

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