Fractional Lion Algorithm-An Optimization Algorithm for Data Clustering

Chander, Satish; Vijaya, P. A.; Dhyani, Praveen

doi:10.3844/jcssp.2016.323.340

Cited by 36 publications

(16 citation statements)

References 17 publications

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“…P-Whale is an optimization algorithm that modifies WOA by the integration of PSO in the update rule to improve the search process, and thereby enhance the performance of the algorithm. WOA [27] is a metaheuristic algorithm that solves optimization problems [28,29] inspired by the physical behaviour of humpback whales, encircling prey, bubble-net attack mechanism and search for prey. In the position update of WOA, the update rule of PSO is adopted, and thus determines a suitable whale fusion factor for each sub-band.…”

Section: Generation Of Whale Fusion Factor Using the Proposed P-whalementioning

confidence: 99%

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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Medical treatment and diagnosis require information that is taken from several modalities of images like Magnetic Resonance Imaging (MRI), Computerized Tomography and so on. The information obtained for certain ailments is often incomplete, invisible and lacking in consistent scanner performance. Hence, to overcome these issues in the image modalities, image fusion schemes are developed in the literature. This paper proposes a hybrid algorithm using fuzzy concept and a novel P-Whale algorithm, called Fuzzy Whale Fusion (FWFusion), for the fusion of MRI multimodal images. Two multimodal images from MRI (T1, T1C, T2 and FLAIR) are considered as the source images, which are fed as inputs to a wavelet transform. The transform utilized converts the images into four different bands, which are fused using two newly derived fusion factors, fuzzy fusion and whale fusion, in a weighted function. The proposed P-Whale approach combines Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for the effective selection of whale fusion factors. The performance of FWFusion model is compared to those of the existing strategies using Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE), as the evaluation metrics. From the mean performance evaluation, it is observed that the proposed approach can achieve MI of 1.714, RMSE of 1.9 and PSNR of 27.9472.

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Section: Generation Of Whale Fusion Factor Using the Proposed P-whalementioning

confidence: 99%

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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“…Multi kernel-based distance measurement can be included for finding the fitness of the cluster process. Further Strengthened with different optimization theory is defined by solution [17].…”

confidence: 99%

An Improved K-Lion Optimization Algorithm With Feature Selection Methods for Text Document Cluster

Jagatheeshkumar¹,

Brunda²

2018

ijcse

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Growth of Computer applications in most of the people and companies are wanted to work through computers. They mostly use computer to store and retrieve information. Data mining is organizing and retrieving information from large data set. Now a day's dataset may be dynamic. Text Document clustering is a passion or an interested area of data mining. Many of the clustering method needed for a new one requires better clustering approaches. A new proposal is an improved KLOA with feature selection method for text mining that is Improved KLOA. K-means is one of the active algorithms for wider application of clustering technique. But it has some inconvenience to form a cluster in the initial point. A novel KLOA algorithm is refined and enhanced by k-means algorithm. This is used to pick the initial point and perform well when some think is rendered. To implement Feature selection method is to find subset and improve the process of cluster. Using Feature selection method is to improve the quality of cluster and find intrinsic properties of dataset. In this new article using wrapper technique of feature selection method is implemented and produces high quality of text clusters, with more accuracy and performance.

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“…Recent research on cluster‐based tree structure helps to collect the data from the nodes with an objective of minimizing the energy consumption of the node 20 . For the proper selection of routes in a tree, optimization algorithms 21,22 may also be preferred 23,24 …”

Section: Introductionmentioning

confidence: 99%

CS‐MAODV: Cuckoo search and M‐tree‐based multiconstraint optimal Multicast Ad hoc On‐demand Distance Vector Routing Protocol for MANETs

Babu

Ussenaiah

2020

Int J Communication

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Mobile ad hoc networks (MANETs) are a subclass of wireless ad hoc networks, having amazing qualities of dynamic network topology and moving nodes. As MANET changes its topology, routing is difficult at some moment. Routing protocol controls the stream of information and selects the efficient way to transmit the data. There are a variety of routing protocols existing in MANET. Accordingly, this paper presents the multicast routing protocol, named cuckoo search and M-tree-based multicast ad hoc on-demand distance vector (CS-MAODV) protocol for MANET. The proposed routing protocol includes a two-step process, namely, M-tree construction and optimal multicast route selection. Here, the divisional-based cluster (DIVC) technique is used for building the M-tree using three constraints: destination flag, path-inclusion factor, and multifactor. Then, cuckoo search algorithm is used for selecting the optimal route by considering the multiple objectives, such as energy, link lifetime, distance, and delay. The performance of the proposed multicast routing protocol is analyzed, and the comparative analysis is performed with the existing routing protocols, such as MAODV, QoS-based multicast routing protocol using reliable neighbor nodes selection (QMRPRNS), efficient fuzzybased multiconstraint protocol (EFMMRP), and energy-efficient lifetime aware multicast route selection (EELAM). From the simulation results, it can be shown that the proposed CS-MAODV protocol attains the maximum energy of 90.3513%, LLT of 158.7708 s, the throughput of 86.2226%, and PDR of 87.1606, respectively. Also, the proposed protocol has the minimum control overhead of 2.1415% and PDD of 0.0563 s.

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Fractional Lion Algorithm-An Optimization Algorithm for Data Clustering

Cited by 36 publications

References 17 publications

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

An Improved K-Lion Optimization Algorithm With Feature Selection Methods for Text Document Cluster

CS‐MAODV: Cuckoo search and M‐tree‐based multiconstraint optimal Multicast Ad hoc On‐demand Distance Vector Routing Protocol for MANETs

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