Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy

In this paper we show that the non-extensive Tsallis entropy, when used as kernel in the bio-inspired firefly algorithm for multi-thresholding in image segmentation, is more efficient than using the traditional cross-entropy presented in the literature. The firefly algorithm is a swarm-based meta-heuristic, inspired by fireflies-seeking behavior following their luminescence. We show that the use of more convex kernels, as those based on non-extensive entropy, is more effective at $$5\,\%$$5% of significance level than the cross-entropy counterpart when applied in synthetic spaces for searching thresholds in global minimum.