The Improvement of Ant Colony Algorithm and Its Application to TSP Problem

Zhang, Jin; Liu, Huaishan; Tong, Siyou

doi:10.1109/wicom.2009.5301753

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…Although traditional routing protocols have performed well in UASNs, they are limited by multiple constraints and a high computational complexity [33]. With the advance of artificial intelligence (AI), many intelligent algorithms have been developed for the routing protocol of UASNs, for example, the artificial fish swim algorithm (AFSA), simulated annealing algorithm (SAA), ant colony algorithm (ACA) [34], and Q-learning algorithm. Although these intelligent algorithms can determine the optimal solution through iteration, they still have the shortcoming of falling into the local optimum because of the long search time.…”

Section: Related Researchmentioning

confidence: 99%

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Tan,

Li,

Sun

et al. 2024

JMSE

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An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption.

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Section: Related Researchmentioning

confidence: 99%

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

Tan,

Li,

Sun

et al. 2024

JMSE

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“…Extract the data with fault characteristics and use the KNN method to judge the fault data in the next step. The KNN method judges the label type of unlabeled data according to the input labeled data [15] . Hereby, the three-phase current data of the inverter can still be input as three-dimensional data.…”

Section: Fault Feature Extractionmentioning

confidence: 99%

An open circuit fault identification method of inverter based on FDA-KNN

Shen

xiaowei

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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The fault of inverters seriously affects individuals' lives, and researchers have become increasingly interested in its method of fault identification. In view of the similarity of three-phase current data of inverter open circuit fault and the low classification accuracy caused by the slight change of three-phase current data measured for distinct open circuit faults, the FDA-KNN method is proposed. Moreover, the method extracts fault characteristics from the three-phase current value of the inverter prior to label discrimination. In the first place, fisher linear discrimination (FDA) is adopted to separate normal data from fault data to extract fault data features, and the subsequently K-nearest neighbor algorithm (KNN) is employed to label faults and identify the open circuit fault type of inverter. The results demonstrate that this method is straightforward to implement and comprehend. It solves the issue of fault classification for similar inverter three-phase current data with greater precision than conventional methods.

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