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

In this paper, a new Class Topper Optimization (CTO) algorithm is proposed. The optimization algorithm is inspired from the learning intelligence of students in a class. The algorithm is population based search algorithm. In this approach, solution is converging towards the best solution. This may l...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2020-10, Vol.8 (4), p.948-959
Main Authors: Das, Pranesh, Das, Dushmanta Kumar, Dey, Shouvik
Format: Article
Language:English
Subjects:
Artificial intelligence
Clustering
Clustering algorithms
Data analysis
data analysis and nature inspired optimization
Data clustering
Heuristic algorithms
Heuristic methods
learning intelligence
Learning systems
Machine learning
Optimization
optimization algorithm
Optimization algorithms
Search algorithms
Standard data
Whales
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Summary:In this paper, a new Class Topper Optimization (CTO) algorithm is proposed. The optimization algorithm is inspired from the learning intelligence of students in a class. The algorithm is population based search algorithm. In this approach, solution is converging towards the best solution. This may lead to a global best solution. To verify the performance of the algorithm, a clustering problem is considered. Five standard data sets are considered for real time validation. The analysis shows that the proposed algorithm performs very well compared to various well known existing heuristic or meta-heuristic optimization algorithms.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2018.2812927