Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

Aljarah, Ibrahim; Mafarja, Majdi; Heidari, Ali Asghar; Faris, Hossam; Mirjalili, Seyedali

doi:10.1007/s10115-019-01358-x

Cited by 74 publications

(18 citation statements)

References 73 publications

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“…The main advantage of their proposal is that the less bright sensor depends on the bright sensor for information transmission. The authors in [6] proposed a locally optimized grey wolf as a cluster optimization scheme. This scheme is prone to local minima and premature convergence having significant variables.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

et al. 2021

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Artificial I ntelligence-based Q uorum s ystems a re used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network's actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively.

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Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

et al. 2021

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“…This algorithm mimics the social leadership and hunting behavior of grey wolves in nature. Where society is divided into four sections, the first of which is alpha and symbolized by the symbol α and beta and symbolized by the symbol β and the delta and symbolized by the symbol δ and omega and symbolized by the symbol ω [12]. Grey wolves live in groups and is called the Alpha Leader, who is responsible for making decisions regarding hunting, waking time, sleeping place, etc.…”

Section: Grey Wolf Optimizer Algorithm Gwomentioning

confidence: 99%

“…To perform a clustering operation for image data using the GWO algorithm, a fitness function is used that calculates the Euclidean distance between the image data values and the centers of the specified clustering from the start of the work according to the following equation [12]:…”

Section: Grey Wolf Optimizer Algorithm Gwomentioning

confidence: 99%

Image Compression Using Swarm Intelligence

Khaleel¹

2021

IJIES

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As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.

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“…Chantar et al used binary GWO, which is a combination of GWO with the future selection process in machine learning for Arabic text classification. Aljarah et al proposed a hybrid algorithm, which is a combination of GWO and tabu search for solving clustering‐based analysis problems. Heidari et al proposed oppositional learning‐based GWO for solving two real‐world problems of key parameter tuning of kernel extreme learning machine, and the proposed algorithm is compared with previous versions of GWO in terms of solution quality and convergence speed.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Int Trans Electr Energ Syst

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Summary In recent years, reactive power compensation techniques are receiving more attention across the globe from both the distribution network operators and research groups due to its ability in the reduction of network power loss, voltage deviation, and enhancement of voltage stability. In this paper, two novel and robust optimization techniques implemented for solving optimal capacitor (OC) allocation problem in various distribution networks are water cycle algorithm (WCA) and grey wolf optimizer (GWO). To crisscross the feasibility, GWO and WCA techniques are tested on practical Indian 28‐bus, 47‐bus, and 52‐bus and standard 33‐bus, 69‐bus, and 85‐bus radial distribution networks with uniformly varying load levels. The simulation results obtained by WCA and GWO are compared with other techniques like inclusion and interchange variable (IIV), direct search algorithm (DSA), and teaching and learning–based optimization (TLBO) and found to be better. In addition, from the obtained results, the algorithm's efficiency is evaluated in terms of exploration and exploitation, and convergence characteristics and computational time are discussed.

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Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

Cited by 74 publications

References 73 publications

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

Image Compression Using Swarm Intelligence

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

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