Clustering using Flying Foxes Optimization Algorithm
Flying Foxes Optimization Algorithm for clustering
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This simplified Matlab demo code shows how to use the new Flying Foxes Optimization Algorithm to solve clustering problems.
The only thing researchers need to do is to replace the data in "mydata.xlsx" with their data, and then run the FFOclustering.m file in the Matlab platform.
Researchers are allowed to use this code in their research projects,
as long they cite as:
Zervoudakis, K., & Tsafarakis, S. (2025). Customer segmentation using flying fox optimization algorithm. Journal of Combinatorial Optimization, 49(1), 1–20. https://doi.org/10.1007/S10878-024-01243-6
AND
Zervoudakis, K., Tsafarakis, S. A global optimizer inspired from the survival strategies of flying foxes. Engineering with Computers (2022). https://doi.org/10.1007/s00366-021-01554-w
For more information: https://sites.google.com/view/kzervoudakis/research/metaheuristics/flying-fox-optimizer
Cite As
Konstantinos Zervoudakis (2026). Clustering using Flying Foxes Optimization Algorithm (https://nl.mathworks.com/matlabcentral/fileexchange/176949-clustering-using-flying-foxes-optimization-algorithm), MATLAB Central File Exchange. Retrieved .
Zervoudakis, K., & Tsafarakis, S. (2025). Customer segmentation using flying fox optimization algorithm. Journal of Combinatorial Optimization, 49(1), 1–20. https://doi.org/10.1007/S10878-024-01243-6
Zervoudakis, K., Tsafarakis, S. A global optimizer inspired from the survival strategies of flying foxes. Engineering with Computers (2022). https://doi.org/10.1007/s00366-021-01554-w
General Information
- Version 1.0.2 (20.8 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
