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 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.