Hyperbolic spaces have recently achieved acceleration in the context of machine learning of their high capacity and tree-likeliness structures, taxonomies, text, and graphs. With the same dimension, a hyperbolic vector can represent richer information than a Euclidean vector. In this paper, we propose Metaheuristic optimization algorithms in hyperbolic space. Considering that the most popular optimization tools have not been generalized in hyperbolic space, we design optimization algorithms according to the specific property of the hyperbolic manifold.

However, a major bottleneck here is the obscurity of hyperbolic space and a better comprehension of its gyrovector operations. We aim to introduce researchers and practitioners in the metaheuristic community to the hyperbolic equivariant of the Euclidean operations necessary to tackle their application to Metaheuristic optimization.

We conduct experiments on various metaheuristic algorithms in hyperbolic space.