ArticleSensors (Basel, Switzerland)2021
Data Clustering Using Moth-Flame Optimization Algorithm.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- LS-BMO-HDBSCAN as a hybrid memetic bacterial intelligence framework for efficient data clustering.Scientific reports · 2025Article
- Enhanced aquila optimizer for global optimization and data clustering.Scientific reports · 2025Article
- Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function.Frontiers in psychiatry · 2025Article
- Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems.Scientific reports · 2024Article
- Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Research on Parameter Optimization Method of Sliding Mode Controller for the Grid-Connected Composite Device Based on IMFO Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems.Sensors (Basel, Switzerland) · 2022Article
- An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC.Sensors (Basel, Switzerland) · 2021Article
- Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering.Computational intelligence and neuroscience · 2021Article
- Multi-membrane search algorithm.PloS one · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.
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Registered trials
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