Abstract
Inspired by the evolution of natural plant distributions, bean optimization algorithm (BOA) is proposed and become an efficient swarm intelligence algorithm. Aiming at the disadvantage of low efficiency of fine search in BOA, an algorithm (DBOA) is proposed by integrating the mutation and selection operators of differential evolution into BOA. The mutation operator enriches the population diversity and improves the local optimization speed of the algorithm. The selection operator further ensures the evolution direction and enhances the optimization accuracy of DBOA. The proposed DBOA has been tested on a set of well-known benchmark problems and compared with other typical swarm intelligence algorithms. The experimental results show that DBOA effectively improves the accuracy and speed of the BOA and has better performance in solving complex optimization problems.
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
Yang, X.S., Deb, S., Fong, S., et al.: From swarm intelligence to metaheuristics: nature-inspired optimization algorithms. Computer 49(9), 52–59 (2016)
Li, A.D., Xue, B., Zhang, M.: A forward search inspired particle swarm optimization algorithm for feature selection in classification. In: 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 786–793 (2021)
Wu, W., Wei, Y.: Guiding unmanned aerial vehicle path planning design based on improved ant colony algorithm. Mechatronic Syst. Control Учpeдитeли: Acta Press 49(1), 48–54 (2021)
Brest, J., Maučec, M.S., Bošković, B.: Differential evolution algorithm for single objective bound-constrained optimization: algorithm. In: IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 1–8 (2020)
Zhang, X., Sun, B., Mei, T., et al.: Post disaster restoration based on fuzzy preference relation and bean optimization algorithm. In: 2010 IEEE Youth Conference on Information, Computing and Telecommunications. IEEE, pp. 271–274 (2010)
Sun, Y., Wang, X., Chen, Y., et al.: A modified whale optimization algorithm for large-scale global optimization problems. Expert Syst. Appl. 114, 563–577 (2018)
Zhang, X.-M., Wang, R.-J., Song, L.-T.: A novel evolutionary algorithm-sees optimization algorithm. PR&AI, 21(05), 677–681 (2008)
Wang, P., Chen, Y.: The optimization alogorithm of SOA during the dispatching of disaster relief supplied. Econ. Res. Guide, (08), 252–253 (2010)
Zhang, X.: Research on a Novel Swarm Intelligence Algorithm Inspired by Beans Dispersal. University of Science and Technology of China (2011)
Zhang, X., Sun, B., Mei, T., Wang, R.: Post-disaster restoration based on fuzzy preference relation and Bean Optimization Algorithm. In: 2010 IEEE Youth Conference on Information, Computing and Telecommunications, pp. 271–274 (2010)
Zhang, X., Wang, H., Sun, B., et al.: The Markov model of bean optimization algorithm and its convergence analysis. Int. J. Comput. Intell. Syst. 6(6), 609–615 (2013)
Zhang, X., Jiang, K., Wang, H., et al.: An improved bean optimization algorithm for solving TSP. In: International Conference on Advances in Swarm Intelligence, pp. 261–267 (2012)
Feng, T., Xie, Q., Hu, H., et al.: Bean optimization algorithm based on negative binomial distribution. Lect. Notes Comput. Sci. 9140, 82–88 (2015)
Feng, T.: Study and Application of Bean Optimization Algorithm on Complex Problem. Master's thesis, University of Science and Technology of China, Hefei, China (2017)
Mohsin, A.: Research on Bean Optimization Algorithm Based on Abundance Distribution Patterns. Anhui Agricultural University (2020)
Liu, H., Zhang, X., Wang, C.: Bean optimization algorithm based on cauchy distribution and parent rotation mechanism. Patt. Recogn. Artif. Intell. 34(07), 581–591 (2021)
Jedrzejowicz, P., Skakovski, A.: Improving performance of the differential evolution algorithm using cyclic decloning and changeable population size. J. Univers. Comput. Sci. 22(6), 874–893 (2016)
Liang, J.J., Qu, B.Y., Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou, China Nanyang Technol. Univ. Singapore, Tech. Rep. 201212(34), 281–295 (2013)
Zhang, X.-M., Jiang, Y., Liu, S.-W.: Hybird coyote optimization with grey wolf optimizer and its application to clustering optimization. Acta Automatica Sinica, 1–17 (2022)
Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)
Acknowledgement
This research was funded by Qinghai Science Foundation under grant number 2020-ZJ-913, Special project of scientific and technological achievements transformation in Qinghai province number 2021-GX-114, Scientific research project of graduate students in Anhui universities number YJS20210087.
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
Hu, Y., Li, Y., Li, T., Xu, J., Liu, H., Zhang, C. (2022). Bean Optimization Algorithm Based on Differential Evolution. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-09677-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09676-1
Online ISBN: 978-3-031-09677-8
eBook Packages: Computer ScienceComputer Science (R0)