Juliano Pierezan

TL;DR: Numerical results and non-parametric statistical significance tests indicate that the Coyote Optimization Algorithm is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.

TL;DR: The results show that the proposed Cultural Coyote Optimization Algorithm (CCOA) outperforms its counterpart for benchmark functions and the convergence analysis shows that the cultural mechanism employed in the CCOA has improved the COA balance between exploration and exploitation.

TL;DR: A binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model is proposed.

TL;DR: A new wrapper based in a “v-shaped” binarization of the classical CSA is proposed, which showed that BCSA achieved very good results in terms of classification accuracy and also selected subsets with a small number of features with a relatively low computational cost.

TL;DR: A modified COA (MCOA) approach based on chaotic sequences generated by Tinkerbell map to scatter and association probabilities tuning and an adaptive procedure of updating parameters related to social condition is proposed that presented competitive solutions when compared with other state-of-the-art metaheuristic algorithms in terms of results quality.