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A New Class Topper Optimization Algorithm with an Application to Data Clustering
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DOI：10.1109/TETC.2018.2812927.png)
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Abstract
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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.
Keywords:
Optimization
Clustering algorithms
Heuristic algorithms
Data analysis
Whales
Dolphins
Sociology
Data clustering
optimization algorithm
learning intelligence
data analysis and nature inspired optimization
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