 In this paper, we present a hybrid optimization method combining the Harris-Hawk optimizer HHO with gradient-based optimization, GBO. This combination allows us to explore the design space more effectively while also reducing the risk of getting stuck in a local minimum. The HHO is a population-based algorithm that mimics the hunting behavior of hawks, dividing the search into two phases, exploration and exploitation. However, the original HHO algorithm struggles in the exploitation phase and can become trapped in a local minimum. To address this issue, we propose using a gradient-based optimization, GBO, like method to select better initial candidates. Additionally, the GBO-like method has the advantage of being able to quickly scan the entire design space, but it is dependent on initial conditions. By combining these two approaches, we are able to achieve a balance between exploring the design space and exploiting the best solution. We then use this hybrid approach to optimize all dielectric metagradings, demonstrating improved performance over the original HHO. This article was authored by Kofi Ed. We are article.tv, links in the description below.