Nature Inspired Partitioning Clustering Algorithms: A Review and Analysis

Saemi, Behzad; Hosseinabadi, Ali Asghar Rahmani; Kardgar, Maryam; Balas, Valentina E.; Ebadi, Hamed

doi:10.1007/978-3-319-62524-9_9

Cited by 14 publications

(5 citation statements)

References 42 publications

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“…29 Further, various researchers have proposed CH selection based on metaheuristic approaches. Saemi et al 30 focused on partitioning clustering algorithms (PCA). The authors comprehensively described the role of nature-inspired algorithms in PCA by examining various parameters, namely, time complexity and clustering accuracy while dealing with real and synthetic data sets.…”

Section: A Brief Survey On Existing Ch Selection Methodsmentioning

confidence: 99%

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

Manchanda

Sharma

2020

Int J Communication

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Summary Wireless sensor network (WSN) suffers from the energy‐limited sensor nodes which consume energy heavily depending upon the magnitude of data which is transmitted or received by the nodes in the network. In this paper, our primary aim is to reduce the quantity of data transmitted to the data‐collecting sink, which helps in the energy preservation and eventually leads to network longevity. To address this concern, in this paper, we propose a novel framework for energy‐efficient compressive data gathering (NFECG) for heterogeneous WSN. NFECG works in four following phases; in the first phase, the cluster head (CH) selection is performed by considering remaining energy, “distance within the nodes and the sink,” and node density; in second phase, sleep scheduling is done among the cluster member nodes; further, in third phase, the compression of the aggregated data is performed at the CH level, and equivalent compressed sparse signals are generated which are transmitted to sink. In the last phase, at the sink, decompression is applied to retrieve the original signals. The simulation of NFECG is performed using MATLAB under two cases of different network area and number of nodes. We examine its performance for various performance metrics and also inspect for its scalable characteristics. The results show that for one of the two cases, it improves stability period and network lifetime by 52.59% and 46.09%, respectively, as compared to energy‐adjusted high‐level data total tree (EHDT) protocol, and also for the other case of network configuration, it acquires supreme performance.

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Section: A Brief Survey On Existing Ch Selection Methodsmentioning

confidence: 99%

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

Manchanda

Sharma

2020

Int J Communication

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“…Regarding the partitional clustering algorithms inspired by nature, Saemi et al (2016), the authors review some of them and compare their performance on some criteria such as time complexity, stability, and clustering accuracy on real and synthetic data sets.…”

Section: Categories Of Clustering Algorithmsmentioning

confidence: 99%

Data Clustering using Two-Stage Eagle Strategy Based on Slime Mould Algorithm

Oucheikh¹,

Touil²,

Fri³

2022

Journal of Computer Science

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Data clustering is considered an important component of data mining which aims to split a given dataset into disjoint groups having the same similarities. The developed techniques for clustering have some challenges to cluster entities in complex search space and most of them aim to maximize the sum of inter-cluster distances and minimize the sum of intracluster distances. This objective function is nonlinear and hard to optimize especially for complex search space. Metaheuristics are becoming a trend for solving this task thanks to their promising results. In this study, the eagle strategy is used to take advantage of the exploration provided by Levy Flight (LF) and the exploitation strength of the Slime Mould Algorithm (SMA) to solve the clustering problem. The SMA algorithm is an efficient technique for solving complex optimization problems which has a high exploitation competence. On the other hand, LF tends to have good exploratory behavior. Our strategy exploits these advantages in a balanced way and through welldesigned rounds to ensure the optimality of the clustering solutions. The proposed method is computationally efficient and inexpensive. It also achieves high accuracy in terms of average, worst, best, and the sum of intracluster distance. The method is also evaluated according to the speed of convergence and using statistical tests, namely Wilcoxon. The obtained results are compared with seven benchmarked metaheuristics, namely Grey Wolf Optimizer (GWO), Slime Mould Algorithm (SMA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Genetic Algorithm (GA) using eighteen datasets of shapes and UCI repositories.

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“…Several meta-heuristic-based clustering algorithms have emerged with nature-inspired designs, namely GA and PSO [ 33 , 44 ]. As discussed earlier, several works on the deployment of multi-UAVs as an aerial base station have employed K -means clustering algorithms to partition users.…”

Section: Clustering Approachesmentioning

confidence: 99%

Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Sawalmeh

Othman

Liu

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.

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Nature Inspired Partitioning Clustering Algorithms: A Review and Analysis

Cited by 14 publications

References 42 publications

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

Data Clustering using Two-Stage Eagle Strategy Based on Slime Mould Algorithm

Power-Efficient Wireless Coverage Using Minimum Number of UAVs

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