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moth-flame-optimization

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JingweiToo / Wrapper-Feature-Selection-Toolbox

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This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.

machine-learning data-mining genetic-algorithm feature-selection ant-colony-optimization differential-evolution cuckoo-search particle-swarm-optimization firefly-algorithm metaheuristics salp-swarm-optimization harris-hawks-optimization bat-algorithm ant-lion-optimizer grey-wolf-optimizer moth-flame-optimization flower-pollination-algorithm whale-optimization-algorithm multi-verse-optimizer
  • Updated Mar 4, 2021
  • MATLAB

ADNANE-RBT / metaheuristics

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This project implements two nature-inspired optimization algorithms: Moth Flame Optimization (MFO) and Honey Badger Optimization (HBO). Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star

python benchmark metaheuristics a-star-algorithm tsplib metaheuristic-optimisation metaheuristic-algorithms moth-flame-optimization honeybadger-algorithms
  • Updated Jun 30, 2024
  • Python

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