Abstract
The Evolutionary Algorithms (EAs) are employed to solve complex, multimodal, and multidimensional real-world optimization problems. The non-deterministic nature and randomness of EAs ascertain a larger diversity of the search heuristic, and hence, promises tremendous potential in reaching a better solution. Genetic Algorithm (GA) is a prime example of an EA. In the present time, with the rapid increase of EAs in numbers, a need for algorithmic evaluation has become prominent. In this paper, we have summarized the algorithmic framework of EAs and presented the computational performances of four recent EAs, tested upon a set of fifteen CEC-2015 benchmark functions.
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
Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1(2), 235–306 (2002)
Yang, X.-S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature & Biologically Inspired computing (NaBIC). IEEE, pp. 210–214 (2009)
Ahmadianfar, I., Bozorg-Haddad, O., Chu, X.: Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf. Sci. 540, 131–159 (2020)
Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms. Wiley, New York (2013)
Weerakoon, S., Fernando, T.: A variant of newton’s method with accelerated third-order convergence. Appl. Math. Lett. 13, 87–93 (2000)
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., Deng, Y.: An improved moth-flame optimization algorithm with hybrid search phases. Knowl.-Based Syst. 191, 105–277 (2020)
Liang, J., Qu, B., Suganthan, P., Chen, Q.: “Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real- parameter single objective optimization. Technical Report201411A. Com- putational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, vol. 29, pp. 625–640 (2014)
Olorunda, O., Engelbrecht, A.P.: “Measuring exploration/exploitation in particle swarms using swarm diversity. In: IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1128–1134 (2008)
Acknowledgements
This work is supported by the Science and Engineering Board (SERB), Department of Science and Technology (DST) of the Government of India under Grant No. ECR/2018/000204 and Grant No. EEQ/2019/000657.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarkar, D., Biswas, A. (2022). Comparative Performance Analysis of Recent Evolutionary Algorithms. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_14
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)