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Hybrid fly optimization tuned artificial neural network for AI-based chess playing system

  • Track 2: Medical Applications of Multimedia
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Abstract

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. 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. 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. The Portable Game Notation (PGN) file acts as the input database comprising the details and the games played by the players over 1 month. 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 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. 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 effectiveness of the proposed method is examined in terms of the evaluation metrics which showed the average accuracy above 98% and minimal computation time of 180 secs.

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  1. G H Raisoni University, Amravati, India

    Vikrant Chole & Vijay Gadicha

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  1. Vikrant Chole
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  2. Vijay Gadicha
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Chole, V., Gadicha, V. Hybrid fly optimization tuned artificial neural network for AI-based chess playing system. Multimed Tools Appl 82, 20453–20475 (2023). https://doi.org/10.1007/s11042-022-14136-9

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