Data Clustering Using Moth-Flame Optimization Algorithm

Singh, Tribhuvan; Saxena, Nitin Kumar; Khurana, Manju; Singh, Dilbag; Abdalla, Mohamed; Alshazly, Hammam

doi:10.3390/s21124086

Cited by 35 publications

(15 citation statements)

References 47 publications

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“…To verify the performance of the algorithm, we compare the MSA with the MVO algorithm, GWO algorithm, MFO algorithm, and ALO algorithm. These algorithms are all newly proposed meta-inspired optimization algorithms in recent years [ 26 , 27 ]. They are widely used [ 28 , 29 ].…”

Section: Resultsmentioning

confidence: 99%

Multi-membrane search algorithm

et al. 2021

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This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.

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Section: Resultsmentioning

confidence: 99%

Multi-membrane search algorithm

et al. 2021

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“…In this paper, a source node finds the CK and DK nodes in the routing table to evaluate the secure routes. Validation, encryption, and trust are all achieved through cryptographic calculations [29][30][31]. The majority of cryptographic frameworks necessitate a key management mechanism that is safe, dynamic, and efficient.…”

Section: Cryptographic Methodsmentioning

confidence: 99%

Data Security-Based Routing in MANETs Using Key Management Mechanism

et al. 2022

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A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire network’s security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols.

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“…In [ 14 ], the authors presented a comprehensive overview of the algorithms of machine learning for embedded systems and mobile computing space. In [ 15 ], the authors presented a heuristic technique based on the moth-flame optimization (MFO) algorithm for resolving the weak exploration problem of the k-means data clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC

Sikandar

Baloch

Hussain

et al. 2021

Sensors

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Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.

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Data Clustering Using Moth-Flame Optimization Algorithm

Cited by 35 publications

References 47 publications

Multi-membrane search algorithm

Multi-membrane search algorithm

Data Security-Based Routing in MANETs Using Key Management Mechanism

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC

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