A New Class Topper Optimization Algorithm with an Application to Data Clustering

Das, Pranesh; Das, Dushmanta Kumar; Dey, Shouvik

doi:10.1109/tetc.2018.2812927

Cited by 74 publications

(25 citation statements)

References 51 publications

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“…Since humans are considered the smartest creatures in solving real-world problems, humaninspired algorithms can also be more successful in solving optimization problems. Some human-inspired algorithms are harmony search (HS) [50], imperialist competitive algorithm (ICA) [51], teaching-learning-based optimization (TLBO) [52], league championship algorithm (LCA) [53], class topper optimization (CTO) [54], presidential election algorithm (PEA) [11], sine-cosine algorithm (SCA) [55], socio evolution & learning optimization algorithm (SELO) [56], team game algorithm (TGA) [57], ludo game-based swarm intelligence (LGSI) [58], heap-based optimizer (HBO) [15], coronavirus optimization algorithm (CVOA) [59], political optimizer (PO) [14], and Lévy flight distribution (LFD) [4]. Some algorithms are inspired by machine learning, reinforcement learning, and learning classifier systems [60][61][62].…”

Section: Evolutionary Swarm Intelligencementioning

confidence: 99%

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Emami

2021

J Supercomput

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In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.

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Section: Evolutionary Swarm Intelligencementioning

confidence: 99%

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Emami

2021

J Supercomput

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“…Enhancement of knowledge at section level and at student level, two types of knowledge changes are presented. The student's efficiency is enhanced by learning from the best student of that class [35]. An imitator's social learning system will learn the actions of various demonstrators [36] in the following manner:…”

Section: A Social Learning Class Topper Optimization (Sl -Cto) Algorithmmentioning

confidence: 99%

Social Learning Class Topper Optimization (SL-CTO) Based Hop Localization Technique for Wireless Sensor Network

Mohanta¹,

Das²

2021

Preprint

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To address the current situation limitation of traditional DV-Hop, we suggested a DV-Hop localization based on a rectification factor using the Social Learning Class Topper Optimization (SL - CTO) algorithm in that paper. In order to adjust the number of hops between beacon nodes, we have implemented a rectification factor in the suggested method. By measuring the dimensions of all the beacons at dumb nodes, the suggested algorithm decreases communication among unknown or dumb and beacon nodes. The model of network imbalance, It is often considered to be demonstrate a applicability of the Proposed approach in the anisotropic network. Simulations have been performed on LabVIEW@2015, and Comparisons were made with conventional DV-Hop, particle swarm optimization-based DV-Hop and runner-root optimization-based DV-Hop for our proposed algorithm. In comparison to current localization methods, simulation outcomes showed that the proposed localization technique reduces computing time, localization error variance and localization error.

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“…Especially in recent years, with the increasing demand for imaging diagnostic applications, medical image data is more in clinical form in the form of large-scale sequence images [ 14 ]. At the same time, the anatomical structure of the human body is very complicated, which makes some traditional clustering methods have some inapplicability in processing such medical sequence images with very complicated structure and very large data volume [ 15 , 16 ].…”

Section: Application Analysis Of Multi-frame Ct Image In Data Batch Cmentioning

confidence: 99%

Multiple Frame CT Image Sequencing Big Data Batch Clustering Method

Wang

Wei

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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CT image diagnosis technology had developed rapidly in China. In clinical testing, large-scale data gradually existed in the form of sequences. Data clustering was the integration of different substances in an image according to certain properties, but there were still many problems in the use of commonly used data clustering methods in medicine. The current sequencing big data clustering method analysis was still in the research stage and had very important significance. This paper proposed a large-scale batch clustering method based on multi-frame CT images, which was compared with the traditional clustering method, and hoped to provide assistance for clinical applications.

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A New Class Topper Optimization Algorithm with an Application to Data Clustering

Cited by 74 publications

References 51 publications

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Social Learning Class Topper Optimization (SL-CTO) Based Hop Localization Technique for Wireless Sensor Network

Multiple Frame CT Image Sequencing Big Data Batch Clustering Method

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