Directional crossover hunger games search with adaptive Lévy diversity for network subculture

Yi Wei; Yingying Cai; Ali Asghar Heidari; Huiling Chen; Yanyu Chen; Yi Wei; Yingying Cai; Ali Asghar Heidari; Huiling Chen; Yanyu Chen

doi:10.3934/era.2024030

Electronic Research Archive

2024, Volume 32, Issue 1: 608-642. doi: 10.3934/era.2024030

Previous Article Next Article

Research article Special Issues

Directional crossover hunger games search with adaptive Lévy diversity for network subculture

1.
School of Humanities, Wenzhou University, Wenzhou 325000, China
2.
College of Economics and Management, Zhejiang Normal University, Jinhua 321004, China
3.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
4.
College of Economics and Management, Zhejiang Normal University, Jinhua 321004, China
† The authors contributed equally to this work.

Received: 02 September 2023 Revised: 28 November 2023 Accepted: 12 December 2023 Published: 08 January 2024

In this paper, we explore and analyze the network subculture in the youth and actively explore the new path of socialist core values to cultivate the values of college students. Through the effective questionnaire survey of college students, the prediction model of decision support is established by improving the metaheuristic algorithms. Hunger games search (HGS) is a metaheuristic algorithm widely used in many fields. However, the method converges slowly and veers toward the local optimum when presented with challenging problems. Therefore, there is room for HGS to develop. We introduce a brand-new HGS variant, denoted as SDHGS. This variant combines the directional crossover mechanism with an adaptive Lévy diversity strategy. The directed crossover mechanism endeavors to harmonize the interplay between exploration and exploitation, while the adaptive Lévy diversity facet enhances the range of variations within the population. The cooperation of these mechanisms within SDHGS concludes in an augmented convergence rate and heightened precision. SDHGS is compared to HGS, seven classic algorithms, and enhanced algorithms on the benchmark function set to evaluate and demonstrate the performance. Besides, various analytical techniques, such as the Friedman test and the Wilcoxon signed-rank test, are considered when analyzing the experimental results. The findings demonstrate that SDHGS with two techniques greatly enhances HGS performance. Finally, SDHGS is applied to discuss the internal relationship that affects the existence of youth subculture and establish a prediction model of decision support.
- hunger games search,
- adaptive Lévy diversity,
- network subculture,
- directional crossover
Citation: Yi Wei, Yingying Cai, Ali Asghar Heidari, Huiling Chen, Yanyu Chen. Directional crossover hunger games search with adaptive Lévy diversity for network subculture[J]. Electronic Research Archive, 2024, 32(1): 608-642. doi: 10.3934/era.2024030

Related Papers:

Abstract

In this paper, we explore and analyze the network subculture in the youth and actively explore the new path of socialist core values to cultivate the values of college students. Through the effective questionnaire survey of college students, the prediction model of decision support is established by improving the metaheuristic algorithms. Hunger games search (HGS) is a metaheuristic algorithm widely used in many fields. However, the method converges slowly and veers toward the local optimum when presented with challenging problems. Therefore, there is room for HGS to develop. We introduce a brand-new HGS variant, denoted as SDHGS. This variant combines the directional crossover mechanism with an adaptive Lévy diversity strategy. The directed crossover mechanism endeavors to harmonize the interplay between exploration and exploitation, while the adaptive Lévy diversity facet enhances the range of variations within the population. The cooperation of these mechanisms within SDHGS concludes in an augmented convergence rate and heightened precision. SDHGS is compared to HGS, seven classic algorithms, and enhanced algorithms on the benchmark function set to evaluate and demonstrate the performance. Besides, various analytical techniques, such as the Friedman test and the Wilcoxon signed-rank test, are considered when analyzing the experimental results. The findings demonstrate that SDHGS with two techniques greatly enhances HGS performance. Finally, SDHGS is applied to discuss the internal relationship that affects the existence of youth subculture and establish a prediction model of decision support.

References

[1]	J. P. Williams, Subculture's not dead! Checking the pulse of subculture studies through a review of 'subcultures, popular music and political change' and 'youth cultures and subcultures: Australian perspectives', Young, 27 (2019), 89–105. https://doi.org/10.1177/1103308818761271 doi: 10.1177/1103308818761271
[2]	R. Baird, Youth and social media: The affordances and challenges of online graffiti practice, Media, Cult. Soc., 44 (2022), 764–784. https://doi.org/10.1177/01634437211069969 doi: 10.1177/01634437211069969
[3]	R. Sugihartati, Youth fans of global popular culture: Between prosumer and free digital labourer, J. Consum. Cult., 20 (2017), 305–323. https://doi.org/10.1177/1469540517736522 doi: 10.1177/1469540517736522
[4]	K. C. Tan, S. Cheng, Sang subculture in post-reform China, Global Media China, 5 (2020), 86–99. https://doi.org/10.1177/2059436420904459 doi: 10.1177/2059436420904459
[5]	X. Liu, J. Zhao, J. Li, B. Cao, Z. Lv, Federated neural architecture search for medical data security, IEEE Trans. Ind. Inf., 18 (2022), 5628–5636. https://doi.org/10.1109/tii.2022.3144016 doi: 10.1109/tii.2022.3144016
[6]	Z. Lv, D. Chen, H. Feng, W. Wei, H. Lv, Artificial intelligence in underwater digital twins sensor networks, ACM Trans. Sens. Netw., 18 (2022), 1–27. https://doi.org/10.1145/3519301 doi: 10.1145/3519301
[7]	L. Liu, D. Zhao, F. Yu, A. A. Heidari, C. Li, J. Ouyang, et al., Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation, Comput. Biol. Med., 136 (2021), 104609. https://doi.org/10.1016/j.compbiomed.2021.104609 doi: 10.1016/j.compbiomed.2021.104609
[8]	F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems, Appl. Intell., 51 (2020), 1531–1551. https://doi.org/10.1007/s10489-020-01893-z doi: 10.1007/s10489-020-01893-z
[9]	F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, W. Al-Atabany, Honey badger algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simul., 192 (2022), 84–110. https://doi.org/10.1016/j.matcom.2021.08.013 doi: 10.1016/j.matcom.2021.08.013
[10]	X. Li, Y. Sun, Stock intelligent investment strategy based on support vector machine parameter optimization algorithm, Neural. Comput. Appl., 32 (2019), 1765–1775. https://doi.org/10.1007/s00521-019-04566-2 doi: 10.1007/s00521-019-04566-2
[11]	F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, S. Mirjalili, Henry gas solubility optimization: A novel physics-based algorithm, Future Gener. Comput. Syst., 101 (2019), 646–667. https://doi.org/10.1016/j.future.2019.07.015 doi: 10.1016/j.future.2019.07.015
[12]	J. Tu, H. Chen, M. Wang, A. H. Gandomi, The colony predation algorithm, J. Bionic Eng., 18 (2021), 674–710. https://doi.org/10.1007/s42235-021-0050-y doi: 10.1007/s42235-021-0050-y
[13]	S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst., 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
[14]	I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, H. Chen, RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method, Expert Syst. Appl., 181 (2021), 115079. https://doi.org/10.1016/j.eswa.2021.115079 doi: 10.1016/j.eswa.2021.115079
[15]	A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
[16]	I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, A. H. Gandomi, INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 195 (2022), 116516. https://doi.org/10.1016/j.eswa.2022.116516 doi: 10.1016/j.eswa.2022.116516
[17]	S. Sharma, S. Chakraborty, A. K. Saha, S. Nama, S. K. Sahoo, mLBOA: A modified butterfly optimization algorithm with lagrange interpolation for global optimization, J. Bionic Eng., 19 (2022), 1161–1176. https://doi.org/10.1007/s42235-022-00175-3 doi: 10.1007/s42235-022-00175-3
[18]	W. Shan, H. Hu, Z. Cai, H. Chen, H. Liu, M. Wang, et al., Multi-strategies boosted mutative crow search algorithm for global tasks: Cases of continuous and discrete optimization, J. Bionic Eng., 19 (2022), 1830–1849. https://doi.org/10.1007/s42235-022-00228-7 doi: 10.1007/s42235-022-00228-7
[19]	X. Zhou, W. Gui, A. A. Heidari, Z. Cai, H. Elmannai, M. Hamdi, et al., Advanced orthogonal learning and Gaussian barebone hunger games for engineering design, J. Comput. Des. Eng., 9 (2022), 1699–1736. https://doi.org/10.1093/jcde/qwac075 doi: 10.1093/jcde/qwac075
[20]	S. L. Li, X. B. Li, H. Chen, Y. X. Zhao, J. W. Dong, A novel hybrid hunger games search algorithm with differential evolution for improving the behaviors of non-cooperative animals, IEEE Access, 9 (2021), 164188–164205. https://doi.org/10.1109/ACCESS.2021.3132617 doi: 10.1109/ACCESS.2021.3132617
[21]	E. H. Houssein, M. E. Hosney, W. M. Mohamed, A. A. Ali, E. M. G. Younis, Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data, Neural. Comput. Appl., 35 (2022), 5251–5275. https://doi.org/10.1007/s00521-022-07916-9 doi: 10.1007/s00521-022-07916-9
[22]	P. Ghasemi, F. Goodarzian, A. Gunasekaran, A. Abraham, A bi-level mathematical model for logistic management considering the evolutionary game with environmental feedbacks, Int. J. Logist. Manage., 34 (2023), 1077–1100. https://doi.org/10.1108/IJLM-04-2021-0199 doi: 10.1108/IJLM-04-2021-0199
[23]	P. Ghasemi, F. Goodarzian, J. Muñuzuri, A. Abraham, A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning, Appl. Math. Model., 104 (2022), 750–781. https://doi.org/10.1016/j.apm.2021.12.023 doi: 10.1016/j.apm.2021.12.023
[24]	P. Ghasemi, F. Goodarzian, A. Abraham, S. Khanchehzarrin, A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network, Appl. Math. Model., 112 (2022), 282–303. https://doi.org/10.1016/j.apm.2022.08.003 doi: 10.1016/j.apm.2022.08.003
[25]	A. babaeinesami, P. Ghasemi, M. Abolghasemian, A. P. chobar, A Stackelberg game for closed-loop supply chains under uncertainty with genetic algorithm and gray wolf optimization, Supply Chain Anal., 4 (2023), 100040. https://doi.org/10.1016/j.sca.2023.100040 doi: 10.1016/j.sca.2023.100040
[26]	S. García, A. Fernández, J. Luengo, F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Inf. Sci., 180 (2010), 2044–2064. https://doi.org/10.1016/j.ins.2009.12.010 doi: 10.1016/j.ins.2009.12.010
[27]	J. Derrac, S. García, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput., 1 (2011), 3–18. https://doi.org/10.1016/j.swevo.2011.02.002 doi: 10.1016/j.swevo.2011.02.002
[28]	A. K. Das, D. K. Pratihar, Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover, Soft Comput., 25 (2021), 5455–5481. https://doi.org/10.1007/s00500-020-05545-9 doi: 10.1007/s00500-020-05545-9
[29]	S. Jadhav, H. He, K. Jenkins, Information gain directed genetic algorithm wrapper feature selection for credit rating, Appl. Soft Comput., 69 (2018), 541–553. https://doi.org/10.1016/j.asoc.2018.04.033 doi: 10.1016/j.asoc.2018.04.033
[30]	F. Tempola, R. Rosihan, R. Adawiyah, Holdout validation for comparison classfication nave bayes and KNN of recipient kartu indonesia pintar, IOP Conf. Ser.: Mater. Sci. Eng., 1125 (2021), 012041. https://doi.org/10.1088/1757-899X/1125/1/012041 doi: 10.1088/1757-899X/1125/1/012041
[31]	X. Zhou, L. Zhang, SA-FPN: An effective feature pyramid network for crowded human detection, Appl. Intell., 52 (2022), 12556–12568. https://doi.org/10.1007/s10489-021-03121-8 doi: 10.1007/s10489-021-03121-8
[32]	X. Liu, S. Wang, S. Lu, Z. Yin, X. Li, L. Yin, et al., Adapting feature selection algorithms for the classification of chinese texts, Systems, 11 (2023), 483. https://doi.org/10.3390/systems11090483 doi: 10.3390/systems11090483
[33]	S. Lu, Y. Ding, M. Liu, Z. Yin, L. Yin, W. Zheng, Multiscale feature extraction and fusion of image and text in VQA, Int. J. Comput. Intell. Syst., 16 (2023), 54. https://doi.org/10.1007/s44196-023-00233-6 doi: 10.1007/s44196-023-00233-6
[34]	E. Emary, H. M. Zawbaa, A. E. Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172 (2016), 371–381. https://doi.org/10.1016/j.neucom.2015.06.083 doi: 10.1016/j.neucom.2015.06.083
[35]	Z. Xu, Z. Hu, A. A. Heidari, M. Wang, X. Zhao, H. Chen, et al., Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis, Expert Syst. Appl., 150 (2020), 113282. https://doi.org/10.1016/j.eswa.2020.113282 doi: 10.1016/j.eswa.2020.113282
[36]	Q. Fan, Z. Zhang, X. Huang, Parameter conjugate gradient with secant equationbased elman neural network and its convergence analysis, Adv. Theor. Simul., 5 (2022), 2200047. https://doi.org/10.1002/adts.202200047 doi: 10.1002/adts.202200047
[37]	L. Cai, L. Xiong, J. Cao, H. Zhang, F. E. Alsaadi, State quantized sampled-data control design for complex-valued memristive neural networks, J. Franklin Inst., 359 (2022), 4019–4053. https://doi.org/10.1016/j.jfranklin.2022.04.016 doi: 10.1016/j.jfranklin.2022.04.016

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)