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dfDB-MRFO: A powerful Meta-Heuristic Optimization Algorithm

version 2.0.2 (2.77 KB) by Hamdi Tolga KAHRAMAN
dynamic FDB Selection Method: An improved version Fitness Distance Balance dfDB-MRFO: Improved Manta Ray Foraging Optimizer with dFDB


Updated 29 Jul 2021

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By using the dynamic FDB selection method, you can transform meta-heuristic search algorithms into a more powerful and efficient method. For this, you should analyze the guide selection process in the meta-heuristic search algorithm and integrate the dynamic FDB selection method into this process. In the uploaded source codes, we applied the dFDB method in the guide selection process of the MRFO algorithm and achieved a unique improvement in the performance of the MRFO. If you read the article that introduces the source codes and the dFDB-MRFO algorithm, you can redesign different algorithms with the dFDB selection method and improve their performance.
Manta Ray Foraging Optimizer has been redesigned using the dFDB method, and thus the dFDB-MRFO algorithm has been developed with improved search performance. dFDB-MRFO is an up-to-date and powerful meta-heuristic search algorithm that can be used to solve single-objective optimization problems.
FDB Selection Method: Fitness Distance Balance was first introduced in the following link:
FDB-based other Meta-heuristic Search Algorithms
FDB-AGDE (An improved version Adaptive Guided Differential Evolution)
FDB-SDO (An improved version of Supply-Demand Optimizer)
LRFDB-COA (An improved version of Coyote Optimization Algorithm)
Levy flight and FDB-based coyote optimization algorithm for global optimization
FDB-SFS (An improved version of Stochastic Fractal Search Algorithm)

Cite As

Kahraman, H. T., Bakir, H., Duman, S., Kati, M., Aras, S., Guvenc, U. Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination. Appl Intell (2021).

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
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