Enhanced Diversity Herds Grey Wolf Optimizer for Optimal Area Coverage in Wireless Sensor Networks

Shieh, Chin‐Shiuh; Nguyen, Trong-The; Wang, Hung-Yu; Dao, Thi-Kien

doi:10.1007/978-3-319-48490-7_21

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…As shown in the equation (17), it is known that w 1 > w 2 > w 3 . This strategy is consistent with the fact that the effects of a, b, and d wolves on x are sequentially reduced.…”

Section: Dynamic Weighting Strategymentioning

confidence: 99%

“…In Pan et al, 16 a new GWOFPA algorithm was proposed, which combines flower pollination algorithm (FPA) and GWO, the position update strategy of FPA improves the direction and speed of grey wolf movement; the author finally verifies the effectiveness of the strategy by testing the benchmark function, but the GWOFPA is more complicated than GWO and has a longer running time. Shieh et al 17 proposed an IGWO algorithm by enriching population diversity. Although the algorithm enhances the ability to jump out of local optimum, the search precision of the solutions is low, and the author only applies it to obstacle-free objects, but the presence of obstacles and static nodes in the actual deployment is not discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Shieh et al. 17 proposed an IGWO algorithm by enriching population diversity. Although the algorithm enhances the ability to jump out of local optimum, the search precision of the solutions is low, and the author only applies it to obstacle-free objects, but the presence of obstacles and static nodes in the actual deployment is not discussed.…”

Section: Introductionmentioning

confidence: 99%

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Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Wang

Xie

et al. 2019

Journal of Algorithms & Computational Technology

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Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

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“…As shown in the equation (17), it is known that w 1 > w 2 > w 3 . This strategy is consistent with the fact that the effects of a, b, and d wolves on x are sequentially reduced.…”

Section: Dynamic Weighting Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Wang

Xie

et al. 2019

Journal of Algorithms & Computational Technology

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“…If the parameter setting is improper, it is easy to deviate from the high-quality solution; Cat Swarm Optimization (CSO) [17,18] mimicked cats hunting behaviour to obtain the optimal solution; Bat Algorithm (BA) [19][20][21] was proposed based on the echolocation behavior of bats; Pigeon Inspired Optimization (PIO) [22,23] was designed by mimicking the homing behavior of the pigeon. It requires very few adjustment parameters and is easy to implement; Symbiotic Organism Search (SOS) [24,25] imitated the interactive population relationship between different organisms in nature to enhance their adaptability to the environment, so as to improve the survival ability of the population; Grey Wolf Optimizer (GWO) [26,27] simulated the hierarchy and predation behavior of wolves. However, it is easy to fall into local extremum and hard to converge; Cuckoo Search (CS) [28][29][30][31] mimicked brood parasitism of cuckoo and it has a strong exploration ability; Monkey King Evolutionary (MKE) [32,33] Algorithm was inspired by the action of the Monkey King, a character of a famous Chinese mythological novel, named Journey to the West.…”

Section: Introductionmentioning

confidence: 99%

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

Chu

Zhuang

et al. 2021

Applied Sciences

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QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

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A Grey Wolf Optimizer for Text Document Clustering

Rashaideh

Sawaie

Al-Betar

et al. 2018

Journal of Intelligent Systems

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Abstract Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. Documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the other comparative clustering methods in terms of measurement criteria, whereby more than 55% of the documents were correctly clustered with a high level of accuracy.

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Enhanced Diversity Herds Grey Wolf Optimizer for Optimal Area Coverage in Wireless Sensor Networks

Cited by 4 publications

References 8 publications

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

A Grey Wolf Optimizer for Text Document Clustering

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