Thus, the ANN’s weights tuned with the proposed Hybrid Fly optimization’s global best solution reduces the training loss thereby, improving the prediction accuracy. The implication of the research relies on the proposed Hybrid Fly optimization algorithm that tunes the ANN classifier’s weight optimally, leading to enhanced system performance by hybridizing the behavior of the Photinus and ephemera flies. The possible legal moves corresponding to the opponent’s single move are evaluated through the proposed method and the mini-max algorithm is utilized for evaluating the best optimal move.

The Portable Game Notation (PGN) file acts as the input database comprising the details and the games played by the players over 1 month. This paper proposes the Hybrid Fly-based artificial neural network (Hybridfly-ANN) model for attaining the maximum possible legal moves by rectifying the drawbacks of conventional position evaluation strategies. While considering the chess game based on AI, the end-to-end pipeline was employed, in which the prior knowledge concerning chess is not essential, but in contrast, the efficient learning makes the method more efficient for making the optimal move. Artificial intelligence (AI) has grown considerably in the present environment and acts as a subject still possessing substantial space for development, making most of its noteworthy advancements in the current era.

Monodevelop xamarin studio