Evolutionary design of a PSO-tuned multigene symbolic regression genetic programming model for river flow forecasting - Sheta, A.; Abdel-Raouf, A.; Fraihat, K.M.; Baareh, A. (International Journal of Advanced Computer Science and Applications, 2023)
The earth’s population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
0 citations (Crossref) [2023-10-31]
Keywords: River flow; dept.csc; evolutionary computation; forecasting; genetic programming; particle swarm optimization; peer-reviewed

Genetic Algorithm Based Service Broker Policy to find Optimal Datacenters in Cloud Services - Chowdhury, S.; Katangur, A.; Sheta, A.; Psayadala, N.R.; Liu, S. (2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, 2023)
In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. This paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. This research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service's response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the CloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms. © 2023 IEEE.
0 citations (Crossref) [2023-10-31]
Keywords: cloud computing; dept.csc; genetic algorithm; service broker policy