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

被引：80
作者：
Das, Pranesh [1 ]
Das, Dushmanta Kumar [2 ]
Dey, Shouvik [3 ]
机构：
[1] Natl Inst Technol Nagaland, Comp Sci & Engn, Dimapur 797103, India
[2] Natl Inst Technol Nagaland, Dimapur 797103, India
[3] Jadavpur Univ, Comp Sci, Kolkata 700103, India
来源：
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING | 2020年 / 8卷 / 04期
关键词：
Optimization; Clustering algorithms; Heuristic algorithms; Data analysis; Whales; Dolphins; Sociology; Data clustering; optimization algorithm; learning intelligence; data analysis and nature inspired optimization; POPULATION-BASED ALGORITHM; SWARM OPTIMIZATION; ROUTING ALGORITHM; SEARCH; INTELLIGENCE;
D O I：
10.1109/TETC.2018.2812927
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：948 / 959
页数：12
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