Evaluation of Genetic Algorithm Optimization by Comparitive Analysis of Error Values

Dr. Mohan Gupta; Mr. Rahul Sharma

doi:https://doi.org/10.51976/jfsa.221902

Submit Manuscript Login / Register Subscribe

Home page

Editorial Board Members

Mission, Aims & Scope

Current Issue

Evaluation of Genetic Algorithm Optimization by Comparitive Analysis of Error Values

Mohan Gupta, Rahul Sharma

https://doi.org/10.51976/jfsa.221902

Author Details ( * ) denotes Corresponding author

1. * Mohan Gupta, Assistant Professor, Department of Mechanical Engineering, United College of Engineering and Research, Prayagraj, Uttar Pradesh, India (mohanguptaucer@gmail.com)

2. Rahul Sharma, Researcher, Tallinn University of Technology, Estonia (rahul.sharma@taltech.ee)

In this paper to test the performance of GA a comparative study has been carried out between PI and PD Controller. The two different objective functions have been chosen that is integral absolute error and integral total absolute error on different generations the controller has been run as a result the minimum value of error has been found in controller in which the objective function was ITAE and a detailed evaluation of Genetic Algorithm has been carried out through out the paper. Optimization and search issues may be solved using genetic algorithms, which are seen as a search process in computers. Global search heuristics are another name for them. Many of these methods are derived from concepts found in evolutionary biology such as mutation, selection, and cross-breeding. For programmes, these algorithms provide a way to automatically enhance their settings. The simulations are tallied to ensure that GA delivers the system promising outcomes.

Keywords

PI Controller; PD Controller; Error Values; IAE; ITAE; Optimization; Genetic Algorithm

Zhou, Q., Leng, G. & Feng, L. Predictability of state-level flood damage in the conterminous United States: the role of hazard, exposure and vulnerability. Scientific reports. 7, 5354 (2017).
Khosravi, K., Pourghasemi, H. R., Chapi, K. & Bahri, M. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental monitoring and assessment. 188, 656 (2016)
Chapi, K. et al. Spatial-temporal dynamics of runoff generation areas in a small agricultural watershed in southern Ontario. Journal of Water Resource and Protection. 7, 14 (2015).
Pradhan, B. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology. 9 (2010).
Tehrany, M. S., Pradhan, B. & Jebur, M. N. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of hydrology. 512, 332–343 (2014)
Kia, M. B. et al. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences. 67, 251–264 (2012).
5. Nayak, P., Sudheer, K., Rangan, D. & Ramasastri, K. Short‐term flood forecasting with a neurofuzzy model. Water Resources Research. 41 (2005).
Lee, S. Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surface Processes and Landforms. 32, 2133–2148 (2007).
3. García-Davalillo, J. C., Herrera, G., Notti, D., Strozzi, T. & Álvarez-Fernández, I. DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study. Landslides. 11, 225–246, https://doi.org/10.1007/s10346-012-0379-8 (2014).
Shirzadi, A. et al. A comparative study between popular statistical and machine learning methods for simulating volume of landslides. Catena. 157, 213–226 (2017).
Zeng, Y., Zhang, Z. & Kusiak, A. Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms. Energy. 86, 393–402 (2015).