Abstract
The optimality ability of any optimization algorithm for different problems is a very important aspect. In this article, we check the optimality ability of newly designed population-based artificial electric field algorithm (AEFA) over 30 unconstrained optimization problems of CEC 2017 benchmark set. The computational results of AEFA are compared with other existing algorithms along with their statistical validation. We also performed the numerical convergence of AEFA over the selected benchmark set which ensure the fast convergence of AEFA over other existing algorithms. This article demonstrates the superiority of AEFA over other existing algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
D.E. Goldberg, J.H. Holland, Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
R. Storn, K. Price, Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE, Oct 1995, pp. 39–43
E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
A. Yadav, K. Deep, J.H. Kim, A.K. Nagar, Gravitational swarm optimizer for global optimization. Swarm Evol. Comput. 31, 64–89 (2016)
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)
M. Dorigo, M. Birattari, Ant Colony Optimization (Springer, Berlin, 2010), pp. 36–39
D. Simon, Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
A. Yadav, AEFA: artificial electricfield algorithm for global optimization. Swarm Evol. Comput. 48, 93–108 (2019)
N.H. Awad, M.Z. Ali, J.J. Liang, B.Y. Qu, P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter. Technical Report (2016)
R. Kommadath, P. Kotecha, Teaching learning based optimization with focused learning and its performance on CEC2017 functions, in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, June 2017, pp. 2397–2403
A.W. Mohamed, A.A. Hadi, A.M. Fattouh, K.M. Jambi, LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems, in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, June 2017, pp. 145–152
A. Kumar, R.K. Misra, D. Singh, Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase, in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, June 2017, pp. 1835–1842
D. Halliday, J. Walker, R. Resnick, Fundamentals of Physics (Wiley, Berlin, 2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anita, Yadav, A., Kumar, N. (2020). Artificial Electric Field Algorithm for Solving Real Parameter CEC 2017 Benchmark Problems. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-3290-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-3290-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3289-4
Online ISBN: 978-981-15-3290-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)