In the proposed algorithm, the Fitness-Distance Balance (FDB) selection method was used to determine the search agents that well know the migration routes and guide the herd. Thus, the FDB-LFD algorithm, which has a much stronger search performance, was developed. The performance of the proposed algorithm was tested and verified on CEC17 and CEC20 benchmark problems for low-, middle- and high-dimensional search spaces. Results of the FDB-LFD was compared to the performance of 11 other powerful and up-to-date metaheuristic search algorithms. According to Friedman statistical test results, the proposed FDB-LFD algorithm ranked first, whereas the LFD was ranked eleventh. This result demonstrated that the changes in the design of the LFD algorithm had been successful.
FDB Selection Method: Fitness Distance Balance was first introduced in the following link:
FDB-SOS (An improved version of Symbiotic Organism Search)
FDB-based other Meta-heuristic Search Algorithms
FDB-TLABC (An improved version of Teaching-Learning-based Artificial Bee Colony)
FDB-AGDE (An improved version of Adaptive Guided Differential Evolution)
FDB-SDO (An improved version of Supply-Demand Optimizer)
LRFDB-COA (An improved version of Coyote Optimization Algorithm)
FDB-SFS (An improved version of Stochastic Fractal Search Algorithm)
dfDB-MRFO: (An improved version of Manta Ray Foraging Optimization)
FDB-SMA: (An improved version of Slime Mould Algorithm)
FDB-RUN: (An improved version of Runge Kutta Optimizer)
引用格式
HUSEYIN BAKIR (2024). FDB-LFD (Improved Lévy Flight Distribution Algorithm) (https://www.mathworks.com/matlabcentral/fileexchange/107090-fdb-lfd-improved-levy-flight-distribution-algorithm), MATLAB Central File Exchange. 检索来源 .
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