Green Roof Optimization Using Multi Objective Optimization with Genetic Programming Based Artificial Neural Network

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Veer-Samara-Sihman:2024:ICIICS,

• author = "Rana {Veer Samara Sihman} and Muntader M. Almusawi and Mohammed {Hussein Fallah} and T. Saravanan and N Rajesh",
• title = "Green Roof Optimization Using Multi Objective Optimization with Genetic Programming Based Artificial Neural Network",
• booktitle = "2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS)",
• year = "2024",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Heating systems, Uncertainty, Urban areas, Artificial neural networks, Feature extraction, Linear programming, Optimisation, Meteorology, Load modelling, artificial neural network, fuzzy if-then rule, green roof system, multi objective optimisation",
• DOI = "doi:10.1109/ICIICS63763.2024.10859609",
• abstract = "In recent years, the greenhouse gases are rising increasingly and the extreme usage of vestige energy resources helped in the green spaces development including green roof systems, which is an important focus in urban areas. In existing, green roof designs are optimised with different methods such as Fuzzy framework-based optimisation. However, the framework is a single object based optimisation so, the framework struggle to handle multi conflict objects. To overcome this issue, Multi Objective Optimisation with Genetic Programming based Artificial Neural Network (MOO-GPANN) is proposed to optimise the green roof design. Initially, the input data is taken from NYC Green Roof Footprints (NYC-GRF) dataset and further processed into feature extraction with fuzzy if-then rule. Then, the optimal features are selected by using Multi Objective Optimisation (MOO) based on ranking diversity. Finally, the prediction done by using Genetic Programming based Artificial Neural Network (GPANN). From the results, the proposed MOO-GPANN model gave better results than existing Adaptive Neuro-Fuzzy Inference System (ANFIS) by providing results for the objective functions including energy consumption (PMV), Heating Load (HL), Cooling Load (CL) and comfort levels (CFL) respectively.",
• notes = "Also known as \cite{10859609}",

}

Genetic Programming entries for Rana Veer Samara Sihman Muntader M Almusawi Mohammed Hussein Fallah T Saravanan N Rajesh

Citations