Black hole: A new heuristic optimization approach for data clustering

Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful algorithms and computational tools for dealing with complex and optimization problems. This paper proposes a new heuristic algorithm that is inspired by the black hole phenomenon. Similar to other population-based algorithms, the black hole algorithm (BH) starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. At each iteration of the black hole algorithm, the best candidate is selected to be the black hole, which then starts pulling other candidates around it, called stars. If a star gets too close to the black hole, it will be swallowed by the black hole and is gone forever. In such a case, a new star (candidate solution) is randomly generated and placed in the search space and starts a new search. To evaluate the performance of the black hole algorithm, it is applied to solve the clustering problem, which is a NP-hard problem. The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.