Abstract
Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: Differential evolution-based neural network training incorporating a centroid-based strategy and dynamic opposition-based learning. In: IEEE Congress on Evolutionary Computation, pp. 2958–2965. IEEE (2016)
Cai, Z., Gong, W., Ling, C.X., Zhang, H.: A clustering-based differential evolution for global optimization. Appl. Soft Comput. 11(1), 1363–1379 (2011)
Fister, I., Fister, D., Deb, S., Mlakar, U., Brest, J., Fister, I.: Post hoc analysis of sport performance with differential evolution. Neural Comput. Appl. 32(15), 10799–10808 (2018). https://doi.org/10.1007/s00521-018-3395-3
Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635 (2013)
Mohamed, A.W., Almazyad, A.S.: Differential evolution with novel mutation and adaptive crossover strategies for solving large scale global optimization problems. Appl. Comput. Intell. Soft Comput. 2017 (2017)
Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search: a new population-based metaheuristic optimization algorithm. Appl. Intell. 47(3), 850–887 (2017). https://doi.org/10.1007/s10489-017-0903-6
Mousavirad, S.J., Rahnamayan, S.: Differential evolution algorithm based on a competition scheme. In: 14th International Conference on Computer Science and Education (2019)
Mousavirad, S.J., Rahnamayan, S.: Enhancing SHADE and L-SHADE algorithms using ordered mutation. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 337–344. IEEE (2020)
Mousavirad, S.J., Rahnamayan, S.: Evolving feedforward neural networks using a quasi-opposition-based differential evolution for data classification. In: IEEE Symposium Series on Computational Intelligence (2020)
Mousavirad, S.J., Rahnamayan, S.: A novel center-based differential evolution algorithm. In: Congress on Evolutionary Computation. IEEE (2020)
Mousavirad, S.J., Rahnamayan, S.: One-array differential evolution algorithm with a novel replacement strategy for numerical optimization. In: International Conference on Systems, Man, and Cybernetics (2020)
Mousavirad, S.J., Rahnamayan, S., Schaefer, G.: Many-level image thresholding using a center-based differential evolution algorithm. In: Congress on Evolutionary Computation (2020)
Mousavirad, S.J., Schaefer, G., Korovin, I.: A global-best guided human mental search algorithm with random clustering strategy. In: International Conference on Systems, Man and Cybernetics, pp. 3174–3179 (2019)
Mousavirad, S.J., Schaefer, G., Korovin, I., Oliva, D.: RDE-OP: a region-based differential evolution algorithm incorporation opposition-based learning for optimising the learning process of multi-layer neural networks. In: 24th International Conference on the Applications of Evolutionary Computation (2021)
Mousavirad, S.J., Zabihzadeh, D., Oliva, D., Perez-Cisneros, M., Schaefer, G.: A grouping differential evolution algorithm boosted by attraction and repulsion strategies for masi entropy-based multi-level image segmentation. Entropy 24(1), 8 (2022)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: IEEE Congress on Evolutionary Computation, pp. 71–78. IEEE (2013)
Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: IEEE Congress on Evolutionary Computation, pp. 1658–1665. IEEE (2014)
Wang, X., et al.: Massive expansion and differential evolution of small heat shock proteins with wheat (triticum aestivum l.) polyploidization. Sci. Rep. 7(1), 1–12 (2017)
Wu, G., Mallipeddi, R., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2016)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Mousavirad, S.J., Moghadam, M.H., Saadatmand, M., Chakrabortty, R., Schaefer, G., Oliva, D. (2022). RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-02462-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02461-0
Online ISBN: 978-3-031-02462-7
eBook Packages: Computer ScienceComputer Science (R0)