Recently, many algorithms have emerged inspired by the biological behavior of animal life to deal with complicated applications such as combinatorial optimization. One of the most critical discussions involving these algorithms is concerning their objective functions. Also, recently, many works have demonstrated the efficiency of Tsallis non-extensive statistics in several applications. However, this formalism has not yet been tested in most recent bio-inspired algorithms as an evaluation function. Thus, this paper presents a study of seven of the most promising bio-inspired algorithms recently proposed (a maximum one decade), from this entropy applied to the multi-thresholding segmentation of medical images. The results show the range of values of q, the so-called non-extensivity parameter of the Tsallis entropy, for which the algorithms tested here have their best performance. It is also demonstrated that the Firefly algorithm (FFA) is the one that obtained the best performance in terms of segmentation, and Grey Wolf Optimizer (GWO) presents the fastest convergence.

最近，受动物生命生物学行为的启发，出现了许多算法来处理诸如组合优化之类的复杂应用。涉及这些算法的最关键的讨论之一是关于它们的目标函数。同样，最近，许多工作证明了Tsallis非广泛统计在几种应用中的效率。但是，这种形式主义尚未在最新的生物启发算法中作为评估函数进行测试。因此，本文介绍了对最近提出的七个最有前途的生物启发算法的研究（最多十年），该算法适用于医学图像的多阈值分割。结果表明q的取值范围，即Tsallis熵的所谓非扩展性参数，此处测试的算法对此具有最佳性能。还证明了Firefly算法（FFA）是在分割方面获得最佳性能的算法，而Gray Wolf Optimizer（GWO）则表现出最快的收敛速度。