View in EDS

Optimizing LSTM Hyperparameters with Whale Optimization Algorithm for Efficient Freight Distribution in Smart Cities

Saved in:
Bibliographic Details
Title: Optimizing LSTM Hyperparameters with Whale Optimization Algorithm for Efficient Freight Distribution in Smart Cities
Authors: Yogesh Kumar Sharma, Bakeeru Mery Sowjanya, Mylapalli Kanthi Rekha, Iyyappan Moorthi, Ajay Kumar, Susheela Hooda
Source: Scalable Computing: Practice and Experience. 26:2196-2213
Publisher Information: Scalable Computing: Practice and Experience, 2025.
Publication Year: 2025
Description: Smart cities save logistics and operational expenses by optimizing freight distribution. This paper presents LSTM hyperparameter adjustment to optimise freight allocation using the Whale Optimization Algorithm (WOA). Traditional hyperparameter tuning struggles with freight logistics’ complexity and dynamism. WOA, a revolutionary bio-inspired optimization approach, finds optimal LSTM network hyperparameters. Our integrated solution fine-tunes LSTM hyperparameters using WOA to increase forecast accuracy and efficiency. The solution is tested on many smart city freight distribution scenarios. To prove the method works, prediction accuracy, computing efficiency, and convergence rate are measured. To determine how well the model detects data patterns and variations, the authors compare anticipated and real traffic flows using MAE, MSE, RMSE, etc. The proposed model’s root mean squared error is 0.23912122600654664 and achieved MAE value of 0.17255859883764077. The WOA-optimized LSTM model outperforms hyperparameter tuning in prediction accuracy and convergence speed. This optimises resource allocation and reduces environmental effect in freight distribution, enabling smart city concepts. These findings affect urban logistics and encourage more investigation.
Document Type: Article
ISSN: 1895-1767
DOI: 10.12694/scpe.v26i5.4945
Rights: CC BY
Accession Number: edsair.doi...........6d33bfbf8cbd7f7a85a9f10f624c4b24
Database: OpenAIRE
Description
Abstract:Smart cities save logistics and operational expenses by optimizing freight distribution. This paper presents LSTM hyperparameter adjustment to optimise freight allocation using the Whale Optimization Algorithm (WOA). Traditional hyperparameter tuning struggles with freight logistics’ complexity and dynamism. WOA, a revolutionary bio-inspired optimization approach, finds optimal LSTM network hyperparameters. Our integrated solution fine-tunes LSTM hyperparameters using WOA to increase forecast accuracy and efficiency. The solution is tested on many smart city freight distribution scenarios. To prove the method works, prediction accuracy, computing efficiency, and convergence rate are measured. To determine how well the model detects data patterns and variations, the authors compare anticipated and real traffic flows using MAE, MSE, RMSE, etc. The proposed model’s root mean squared error is 0.23912122600654664 and achieved MAE value of 0.17255859883764077. The WOA-optimized LSTM model outperforms hyperparameter tuning in prediction accuracy and convergence speed. This optimises resource allocation and reduces environmental effect in freight distribution, enabling smart city concepts. These findings affect urban logistics and encourage more investigation.
ISSN:18951767
DOI:10.12694/scpe.v26i5.4945