A Nature Inspired Swarm based Stellar-mass Black hole for Engineering Optimization

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. The nature-inspired metaheuristic algorithms are on swarm intelligence, biological, physical and chemical characteristics depending on origins of inspiration. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black hole which is presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the evolutionary algorithms Genetic Algorithm (GA) and Differential Evolution (DE). The experiment results show that the SBO outperforms GA and DE methods.