Abstract
In this chapter, fundamental concepts of optimization process are explained. Types of optimization problems and algorithms have been explained. Designing of objective function, decision variables, and different solution approaches are also elaborated. Overview of swarm and evolutionary optimization algorithms has been provided with detailed explanation of genetic algorithm, artificial bee colony algorithm, and particle swarm optimization algorithm. Few benchmark objective functions are explained with their equations and MatLab codes. Complete process of optimization of these objective functions through genetic algorithm has been demonstrated with working MatLab codes. Comparison of the performance of optimization methods has also been explained with few performance measures. After reading this chapter and practicing the example codes given in this chapter, readers will be able to apply optimization on problems of their field of interest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
X.-S. Yang, Nature-inspired metaheuristic algorithms (Luniver Press, Frome, 2010)
X.-S. Yang, Engineering optimization: an introduction with metaheuristic applications (Wiley, New York, 2010)
A. Chakraborty, A.K. Kar, Swarm intelligence: A review of algorithms, in Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, ed. by S. Patnaik, X. S. Yang, K. Nakamatsu, vol. 10, (Springer, Cham, 2017). https://doi.org/10.1007/978-3-319-50920-4_19
M.K. Ahirwal, A. Kumar, G.K. Singh, EEG/ERP adaptive noise canceller design with Controlled Search Space (CSS) approach in Cuckoo and other optimization algorithms. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(6), 1491–1504 (2013). https://doi.org/10.1109/TCBB.2013.119
M. Srinivas, L.M. Patnaik, Genetic algorithms: A survey. Computer 27(6), 17–26 (1994)
T. Yalcinoz, H. Altun, M. Uzam, Economic dispatch solution using a genetic algorithm based on arithmetic crossover, in Proceedings of Conference on Porto Power Technology, IEEE, vol. 2, 2011, pp. 1–4
S.A.M. Fahad, M.E. El-Hawary, Overview of Artificial Bee Colony (ABC) algorithm and its applications, in Proceedings of International Conference on Systems, IEEE, 2012, pp. 1–6
B. Akay, D. Karaboga, A modified artificial bee colony algorithm for real-parameter optimization. Inform. Sci. 192, 120–142 (2012)
J. Kennedy, R.C. Eberhart, Particle swarm optimization, in Proceedings of international conference on Neural Networks, IEEE, vol. 4, 1995, pp. 1942–1948
R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization: An overview. Swarm Intell. 1, 33–57 (2007)
S. Surjanovic, D. Bingham, Virtual library of simulation experiments: Test functions and datasets (2013), http://www.sfu.ca/~ssurjano. Accessed 10 Oct 2020
M. Jamil, X.-S. Yang, A literature survey of benchmark functions for global optimisation problems. Int. J. Math. Model. Numer. Optim. 4(2), 150–194 (2013)
M.K. Ahirwal, A. Kumar, G.K. Singh, Adaptive filtering of EEG/ERP through noise cancellers using an improved PSO algorithm. Swarm Evol. Comput. 14, 76–91 (2014)
M.K. Ahirwal, A. Kumar, G.K. Singh, Improved range selection method for evolutionary algorithm based adaptive filtering of EEG/ERP signals. Neurocomputing 144, 282–294 (2014)
M.K. Ahirwal, A. Kumar, G.K. Singh, Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digit. Signal Process. 25, 164–172 (2014)
M.K. Ahirwal, A. Kumar, G.K. Singh, Analysis and testing of PSO variants through application in EEG/ERP adaptive filtering approach. Biomed. Eng. Lett. 2(3), 186–197 (2012)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s)
About this chapter
Cite this chapter
Ahirwal, M.K., Kumar, A., Singh, G.K. (2021). Swarm and Evolutionary Optimization Algorithm. In: Computational Intelligence and Biomedical Signal Processing. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-67098-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-67098-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67097-9
Online ISBN: 978-3-030-67098-6
eBook Packages: EngineeringEngineering (R0)