An Overview of Genetic Algorithm Applied to Control Engineering Problem

Authors

  • Kumud Kant Awasthi Associate Professor, Department of Life Sciences, Vivekananda Global University, Jaipur, India Author

Keywords:

Adaptive, Control Engineering, Evolutionary Computation, Genetic Algorithms, Multiobjective

Abstract

The most well-known evolutionary search  methods are Genetic Algorithms (GAs). Despite the fact  that they are often used in control engineering issues, they  are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to  regulator production issues now available, as well as the  fact that they are often reported on at computer science  conferences rather than control engineering conferences.  This article tries to address this gap by providing an  overview of current GA applications in the area of control  engineering. The hereditary calculation (GA) is a model or  deliberation of organic development in light of Charles  Darwin's hypothesis of normal choice, made by John  Holland and his associates during the 1960s and 1970s.  Holland is broadly attributed similar to the first to utilize  hybrid and recombination, change, and choice to the  investigation of versatile and counterfeit frameworks. The  hereditary calculation as a critical thinking strategy is  deficient without these hereditary administrators. From that  point forward, an assortment of hereditary calculation  variations have been created and applied to an assortment  of advancement issues, going from chart shading to design  acknowledgment, from discrete frameworks (like the  mobile sales rep issue) to persistent frameworks (e.g., the  effective plan of airfoils in advanced plane design), and  from monetary business sectors to multi-objective  designing streamlining. 

Downloads

Download data is not yet available.

References

Q. Wang, P. Spronck, and R. Tracht, “An overview of genetic algorithms applied to control engineering problems,” in International Conference on Machine Learning and Cybernetics, 2003.

P. G. Alotto et al., “Stochastic algorithms in electromagnetic optimization,” IEEE Trans. Magn., 1998.

A. Mellit, S. A. Kalogirou, L. Hontoria, and S. Shaari, “Artificial intelligence techniques for sizing photovoltaic systems: A review,” Renewable and Sustainable Energy Reviews. 2009.

D. Laha, “Heuristics and metaheuristics for solving scheduling problems,” in Handbook of Computational Intelligence in Manufacturing and Production Management, 2007.

A. G. Daful et al., “PID Control Tuning VIA Particle Swarm Optimization for Coupled Tank System,” IFAC Proc. Vol., 2014.

I. A. Wani, I. M. Sheikh, T. Maqbool, and V. Kumar, “Experimental investigation on using plastic wastes to enhance several engineering properties of soil through stabilization,” in Materials Today: Proceedings, 2021.

Ch. Turner and A. Tiwari, “Applications of Soft Computing: Updating the State of the Art,” in Applications of Soft Computing: Updating the State of the Art, 2009.

B. S. K. K. Ibrahim, M. O. Tokhi, M. S. Huq, and S. C. Gharooni, “Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion,” Int. J. Integr. Eng., 2011.

T. Uhl, W. M. (Wiesław M. . Ostachowicz, and J. Holnicki Szulc, Structural health monitoring 2008 : proceedings of the fourth European workshop. 2010.

M. S. Solanki, D. K. P. Sharma, L. Goswami, R. Sikka, and V. Anand, “Automatic Identification of Temples in Digital Images through Scale Invariant Feature Transform,” in 2020 International Conference on Computer Science, Engineering and Applications, ICCSEA 2020, 2020.

H. G. Sheng, “New Theories and Methods for Secondary Voltage Control of Power System,” 2003.

D. Singh, “Robust controlling of thermal mixing procedure by means of sliding type controlling,” Int. J. Eng. Adv. Technol., 2019.

C. Zune et al., “Part list,” Prog. Org. Coatings, 2005. [14] C. S. Yadav, M. Yadav, P. S. S. Yadav, R. Kumar, S. Yadav, and K. S. Yadav, “Effect of Normalisation for Gender

Identification,” in Lecture Notes in Electrical Engineering, 2021.

V. Anand, “Photovoltaic actuated induction motor for driving electric vehicle,” Int. J. Eng. Adv. Technol., 2019.

V. Jain and R. Garg, “Asset management system for improvising the efficiency of biomedical engineering department in hospital,” Pravara Med. Rev., 2018.

N. Mangal, “Transfer Learning Based Activity Recognition using ResNet 101 C-RNN Model,” Int. J. Adv. Trends Comput. Sci. Eng., 2020.

V. Jain, M. Goyal, and M. S. Pahwa, “Modeling the relationship of consumer engagement and brand trust on social media purchase intention-a confirmatory factor experimental technique,” Int. J. Eng. Adv. Technol., 2019.

K. K. Gola, M. Dhingra, and R. Rathore, “Modified version of playfair technique to enhance the security of plaintext and key using rectangular and substitution matrix,” Int. J. Eng. Adv. Technol., 2019.

J. S. Kushawaha and B. K. Misra, “Improved imposition of displacement boundary conditions in element free Galerkin method using penalty method,” Int. J. Comput. Aided Eng. Technol., 2016.

G. Goswami and P. K. Goswami, “A design analysis and implementation of PI, PID and fuzzy supervised shunt APF at nonlinear load application to improve power quality and system reliability,” Int. J. Syst. Assur. Eng. Manag., 2021.

Published

2021-09-30

Issue

Vol. 9 No. 5 (2021): International Journal of Innovative Research in Computer Science and Technology (Sep) 2021

Section

Articles

How to Cite

An Overview of Genetic Algorithm Applied to Control Engineering Problem . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(5), 62–66. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11320