Abstract
The beetle antennae search algorithm (BAS) is inspired by the search behavior of long-horned beetles and is widely used in optimization problems. However, in the typical BAS algorithm, there are problems such as the insufficient optimization ability of beetle individuals and the lack of consideration of historical dynamic information in the algorithm search process, resulting in sufferings from local optimum, low diversity, and imbalanced utilization, etc. To solve these problems, an improved beetle swarm antennae search algorithm based on multiple operators, dubbed MO-BSAS, is proposed. First, the elite opposition-based learning is used to initialize the beetle population, which improves the population diversity and optimization ability of the algorithm; then, the Lévy Flight strategy is used to improve the traditional beetle moving operator. In addition, a multi-operator search strategy determined by the damped sinusoidal probability factor is designed to balance the exploitation and exploration capabilities of the algorithm and speed up its convergence. The MO-BSAS algorithm is compared with BSA and 8 popular or state-of-the-art smart algorithms under 14 different benchmark functions, and the experimental results show that the MO-BSAS algorithm has a shorter convergence time and better convergence accuracy than the algorithms compared, which verifies the effectiveness of the improved algorithm.
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References
Abd EM, Elsheikh AH, Oliva D et al (2022) Advanced metaheuristic techniques for mechanical design problems. Arch Comput Methods in Eng 29(1):695–716. https://doi.org/10.1007/s11831-021-09589-4
Ali W, Anas A, Ali K et al (2020) Problem definitions and evaluation criteria for the cec 2021 special session and competition on single objective bound constrained numerical optimization. Technical Report
Chen TT, Yin H, Jiang HL et al (2019) Particle swarm optimization algorithm based on beetle antennae search for solving portfolio problem. Comput Syst Appl 28(2):171–176
Cheng L, Yu M, Yang J et al (2019) An improved artificial bee colony algorithm based on beetle antennae search. In: 2019 Chinese control conference (CCC), IEEE, pp 2312–2316
Derrac J, García S, Molina D et al (2011) 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. https://doi.org/10.1016/j.swevo.2011.02.002
Dokeroglu T, Sevinc E, Kucukyilmaz T et al (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137(106):040. https://doi.org/10.1016/j.cie.2019.106040
García-Martínez C, Gutiérrez PD, Molina D et al (2017) Since cec 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness. Soft Comput 21(19):5573–5583. https://doi.org/10.1007/s00500-016-2471-9
Happ M, Bathke AC, Brunner E (2019) Optimal sample size planning for the Wilcoxon–Mann–Whitney test. Stat Med 38(3):363–375. https://doi.org/10.1002/sim.7983
Jiang XY, Li S (2018) Bas: Beetle antennae search algorithm for optimization problems. Int J Robot Control 1(1):1. https://doi.org/10.5430/ijrc.v1n1p1
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. https://doi.org/10.1016/j.amc.2009.03.090
Kiani F, Seyyedabbasi A, Nematzadeh S et al (2022) Adaptive metaheuristic-based methods for autonomous robot path planning: sustainable agricultural applications. Appl Sci 12(3):943. https://doi.org/10.3390/app12030943
Li XJ, Zang ZN, Shen F et al (2020) Task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks. Mob Netw Appl 25(6):2517–2526. https://doi.org/10.1007/s11036-020-01593-5
Liao L, Ou-Yang ZY (2021) Beetle antennae search based on quadratic interpolation. Appl Res Comput 38(3):745–750. https://doi.org/10.19734/j.issn.1001-3695.2020.03.0052
Ling Y, Zhou Y, Luo Q (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186. https://doi.org/10.1109/ACCESS.2017.2695498
Liu Y, Cao B (2020) A novel ant colony optimization algorithm with lévy flight. IEEE Access 8:67,205-67,213. https://doi.org/10.1109/ACCESS.2020.2985498
Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Phys Rev E 49(5):4677. https://doi.org/10.1103/physreve.49.4677
Myszkowski PB, Olech LP, Laszczyk M et al (2018) Hybrid differential evolution and greedy algorithm (degr) for solving multi-skill resource-constrained project scheduling problem. Appl Soft Comput 62:1–14. https://doi.org/10.1016/j.asoc.2017.10.014
Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18. https://doi.org/10.1016/j.swevo.2013.11.003
Shao LS, Han RD (2018) Beetle antenna search flower pollination algorithm. Comput Eng Appl 54:188–194
Shi YH, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). IEEE, pp 69–73. https://doi.org/10.1109/ICEC.1998.699146
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328
Ting TO, Yang XS, Cheng S et al (2015) Hybrid metaheuristic algorithms: past, present, and future. Recent Adv Swarm Intell Evolut Comput 585:71–83. https://doi.org/10.1007/978-3-319-13826-8_4
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE, pp 695–701. https://doi.org/10.1109/CIMCA.2005.1631345
Wang JY, Chen HX (2018) Bsas: beetle swarm antennae search algorithm for optimization problems. arXiv:1807.10470
Wang HM, Liu ZB, Qu D (2019) Beetle antennae search algorithm based on pso and fibonacci. In: Proceedings of the 2019 international conference on computer, network, communication and information systems (CNCI 2019). Atlantis Press, pp 259–263. https://doi.org/10.2991/cnci-19.2019.38
Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34. https://doi.org/10.1080/21642583.2019.1708830
Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, pp 240–249. https://doi.org/10.1007/978-3-642-32894-7_27
Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690
Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102. https://doi.org/10.1109/4235.771163
Zhang YY, Li S, Xu B (2021) Convergence analysis of beetle antennae search algorithm and its applications. Soft Comput 25(16):1–14. https://doi.org/10.1007/s00500-021-05991-z
Zheng Y, Huang Z, Tao J et al (2021) A novel chaotic fractional-order beetle swarm optimization algorithm and its application for load-frequency active disturbance rejection control. IEEE Trans Circuits Syst II Express Briefs 69(3):1267–1271. https://doi.org/10.1109/TCSII.2021.3100853
Zhou X, Wu Z, Wang H (2012) Elite opposition-based differential evolution for solving large-scale optimization problems and its implementation on gpu. In: 2012 13th international conference on parallel and distributed computing, applications and technologies, pp 727–732. https://doi.org/10.1109/PDCAT.2012.70
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This work was supported in part by the Discipline Development Fund from School of Science, Jiangxi University of Science and Technology.
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SL and LW performed code development, Kuntao Ye was involved in planning and supervising the work, SL and XZ processed the experimental data, and Leilei Shu performed the analysis. YK and SL drafted the manuscript and designed the figures. YK and SL aided in interpreting the results and worked on the manuscript. All authors discussed the results and commented on the manuscript.
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Ye, K., Shu, L., Xiao, Z. et al. An improved beetle swarm antennae search algorithm based on multiple operators. Soft Comput 28, 6555–6570 (2024). https://doi.org/10.1007/s00500-023-09500-2
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DOI: https://doi.org/10.1007/s00500-023-09500-2