An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

ESMR4FBP: A pLM-based regression prediction model for specific properties of food-derived peptides optimized multiple bionic metaheuristic algorithms

Due to the growing emphasis on food safety, peptide research is increasingly focusing on food sources. Traditional methods for determining peptide properties are expensive. While artificial intelligence (AI) models can reduce cost, existing peptide models often lack accuracy. This study aimed to develop a regression model capable of predicting peptide properties. We integrated the ESM-2 model with the LSTM architecture and optimized the model structure using three metaheuristic algorithms, including WOA, SSA, and HHO. Using an antioxidant tripeptide dataset, our model achieved an R2 of 0.9458 and RMSE of 0.3135, outperforming the state-of-the-art (SOTA) model by 11.66 % and 50.00 %, respectively. The developed model was further applied to the bitter peptide dataset, resulting in R2 of 0.8385 and RMSE of 0.4414, respectively. These results suggest that our model has the potential to accurately predict the properties of various types of peptides.

Application of Heuristic Algorithms in the Tomography Problem for Pre-Mining Anomaly Detection in Coal Seams

The paper presents research on a specific approach to the issue of computed tomography with an incomplete data set. The case of incomplete information is quite common, for example when examining objects of large size or difficult to access. Algorithms devoted to this type of problems can be used to detect anomalies in coal seams that pose a threat to the life of miners. The most dangerous example of such an anomaly may be a compressed gas tank, which expands rapidly during exploitation, at the same time ejecting rock fragments, which are a real threat to the working crew. The approach presented in the paper is an improvement of the previous idea, in which the detected objects were represented by sequences of points. These points represent rectangles, which were characterized by sequences of their parameters. This time, instead of sequences in the representation, there are sets of objects, which allow for the elimination of duplicates. As a result, the reconstruction is faster. The algorithm presented in the paper solves the inverse problem of finding the minimum of the objective function. Heuristic algorithms are suitable for solving this type of tasks. The following heuristic algorithms are described, tested and compared: Aquila Optimizer (AQ), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Dynamic Butterfly Optimization Algorithm (DBOA). The research showed that the best algorithm for this type of problem turned out to be DBOA.