Artificial neural network and differential evolution methodologies used in single – and multi – objective formulations for the solution of subsurface water management problems
Nikolos Ioannis, Papadopoulou Maria P., Karatzas Giorgos
Το work with title Artificial neural network and differential evolution methodologies used in single – and multi – objective formulations for the solution of subsurface water management problems by Nikolos Ioannis, Papadopoulou Maria P., Karatzas Giorgos is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
Ι.K. Nikolos , M.P. Papadopoulou, and G.P. Karatzas, "Artificial neural network
and differential evolution methodologies used in single – and multi – objective
formulations for the solution of subsurface water management problems," International Journal of Advanced Intelligence Paradigms, vol. 2, no.4, pp. 365 - 377, 2010. doi: 10.1504/IJAIP.2010.036401
https://doi.org/10.1504/IJAIP.2010.036401
A single-objective Differential Evolution (DE) algorithm is combined with an Artificial Neural Network (ANN) to examine different operational strategies to cover the water demand in the Northern part of Rhodes Island, Greece. Successive calls to the simulator are used to provide the training data to the ANN, which is used as an approximation model to the simulator. Additionally, a multi-objective DE algorithm is combined with the pre-trained ANN to solve the same problem; the environmental constraints are realised through the definition of a second objective function, whereas the first objective function is the total pumping of the supply wells.