Zhifang Pan

TL;DR: The first powerful variant of the Harris hawks optimization (HHO) integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy and is compared with a range of other methods.

TL;DR: This paper develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL, which could reach higher classification accuracy and fewer feature selections than other optimization algorithms.

TL;DR: In this article, a swarm-based stochastic optimizer with a dispersed foraging strategy is proposed to enhance the slime mold algorithm and maintain population diversity, and the experimental results reveal that the BDFSMA performs better than the original SMA, and that, compared with other optimization algorithms, it improves classification accuracy and reduces the number of selected features.

TL;DR: In this article , an advanced shuffled frog leaping algorithm (DSSRLFLA) is developed for model evaluation and feature selection, which incorporates a dynamic step size adjustment strategy based on historical information, a specular reflection learning mechanism, and a simulated annealing mechanism based on chaotic mapping and levy flight.