摘要：Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by the supernova phenomena in nature. SO mimics this natural phenomena aiming to improve the three main features of optimization; exploration, exploitation, and local minima avoidance. The proposed meta-heuristic optimizer has been tested over 20 will known benchmarks functions, the results have been verified by a comparative study with the state of art optimization algorithms Grey Wolf Optimizer (GWO), A Sine Cosine Algorithm for solving optimization problems (SCA), Multi-Verse Optimizer (MVO), Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (MFO), The Whale Optimization Algorithm (WOA), Polar Particle Swarm Optimizer (PLOARPSO) and with Particle Swarm Optimizer (PSO). The results showed that SO provided very competitive and effective results. It outperforms the best state-of-art algorithms that are compared to on the most of the tested benchmark functions.
