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.

中文翻译：

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一种适用于数据聚类的新类 Topper 优化算法

在本文中，提出了一种新的类 Topper 优化 (CTO) 算法。优化算法的灵感来自于班级学生的学习智能。该算法是基于人口的搜索算法。在这种方法中，解决方案正在向最佳解决方案收敛。这可能会导致全球最佳解决方案。为了验证算法的性能，考虑了一个聚类问题。考虑了五个标准数据集进行实时验证。分析表明，与各种众所周知的现有启发式或元启发式优化算法相比，所提出的算法性能非常好。