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This paper aims to study two approximate analytical methods for solving linear and nonlinear delay differential equations. Approximate approaches are shown in the Variational iteration method and the Adomian decomposition method. Through the conversion of some instances, including linear and nonlinear delay differential equations with initial values, by comparing different approximate methods. The results show that this procedure is accurate and adequate for DDE. Comparisons between the Adomian Decomposition Method and Variational Iteration Method results demonstrate the accuracy of the results obtained from both mentioned methods. The variational iteration method is efficient and convenient based on comparing the results and exact solutions. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business. [ABSTRACT FROM AUTHOR]