[1] M. Arca, A. Papachristoforou, F. Mougel, A. Rortais, K. Monceau, O. Bonnard, P. Tardy, D. Thiery, JF. Silvain and G. Arnold, Defensive behaviour of Apis mellifera against Vespa velutina in France: testing whether European honeybees can develop an effective collective defence against a new predator, Behavioural Process. 1(106) (2014) 9–122.
[2] M. Alam, A, khan and I. Khan, Swarm intelligence in Menets: a survey, Int. J. Emerg. Res. Manag. Technol. 5(5) (2016) 141–150.
[3] B. Bai, Z. Guo, C. Zhou, W. Zhang and J. Zhang, Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering, Inf. Sci. 546 (2021) 42–59.
[4] D. Bairathi and D. Gopalani, A novel swarm intelligence based optimization method: Harris Hawk optimization, Int. Conf. Intell. Syst. Design Appl. 2018, pp. 832–842.
[5] J. Bansal and S. Singh, A better exploration strategy in grey wolf optimizer, J. Ambient Intell. Humanized Comput. 12(1) (2021) 1099-1118.
[6] O. Bello and S. Zeadally, Communication issues in the Internet of Things (IoT), In Next-Generation Wireless Technologies, Springer, London, (2013) 189–219.
[7] E. Bonabeau, M. DDRDF, M. Dorigo, G Th´eraulaz and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999.
[8] B. Cao, X. Wang, W. Zhang, H. Song and Z. Lv, A many-objective optimization model of industrial internet of things based on private blockchain, IEEE Network. 34(5) (2020) 78–83.
[9] B. Cao, J. Zhao, Y. Gu, Y. Ling and X. Ma, Applying graph-based differential grouping for multiobjective largescale optimization, Swarm Evol Comput. 53 (2020) 100626.
[10] G. Ding, Y. Qiao, W. Yi, W. Fang and L. Du, Fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image, J. Ambient Intell. Humanized Comput. 12(1) (2021) 1517–1539.
[11] L. Ding, S. Li, H. Gao, Y. Liu, L. Huang and Z. Deng, Adaptive neural network-based finite-time online optimal tracking control of the nonlinear system with dead zone, IEEE Trans. Cybernet. 2019.
[12] M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Mag. 1(4) (2006) 28–39.
[13] A. Gandomi and A. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numerical Simul. 17(12) (2012) 4831–4845.
[14] J. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, Evol. Comput. 8(4) (1992) 373–391.
[15] B. Hu and B. Yang, A particle swarm optimization algorithm for multi-row facility layout problem in semiconductor fabrication, J. Ambient Intell. Humanized Comput. 10(8) (2019) 3201–3210.
[16] M. Janah and Y. Fujimoto, Study comparison between firefly algorithm and particle swarm optimization for SLAM problems, Int. Power Electron. Conf. (IPEC-Niigata 2018-ECCE Asia), 2018, pp. 681–687.
[17] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. ICNN’95-Int. Conf. Neural Networks, 1995, pp. 1942–1948.
[18] J. Liu, C. Wu, G. Wu and X. Wang, A novel differential search algorithm and applications for structure design, Appl. Math. Comput. 268 (2015) 246–269.
[19] S. Liu, F. Chan and W. Ran, Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes, Expert Syst. Appl. 55 (2016) 37–47.
[20] Z. Lv and L. Qiao, Deep belief network and linear perceptron-based cognitive computing for collaborative robots, Appl. Soft Comput. 92 (2020) 106300.
[21] H. Ma, L. Xu and G. Yang, Multiple environment integral reinforcement learning-based fault-tolerant control for affine nonlinear systems, IEEE Trans Cybernet. 2019.
[22] M. Mavrovouniotis, C. Li and S. Yang, A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm Evol. Comput. 33 (2017) 1–17.
[23] S. Mirjalili, S. Mirjalili and A. Lewis, Grey wolf optimizer, Adv. Engin. Software 69 (2014) 46–61.
[24] S. Mirjalili and A. Lewis, The whale optimization algorithm, Adv. Engin. Software 95 (2016) 51–67.
[25] S. Thennarasu, M. Selvam and K. Srihari, A new whale optimizer for workflow scheduling in cloud computing environment, J. Ambient Intell. Humanized Comput. 12(3) (2021) 3807–3814.
[26] B. Wang, B. Zhang, X. Liu and F. Zou, Novel infrared image enhancement optimization algorithm combined with DFOCS, Optik 224 (2020).
[27] D. Wang, D. Tan and L. Liu, Particle swarm optimization algorithm: an overview, Soft Comput. 22(2) (2018) 387–408.
[28] X. Wang, Y. Zhan, L. Wang and L. Jiang, Ant colony optimization and ad-hoc on-demand multipath distance vector (AOMDV) based routing protocol, Fourth Int. Conf. Natural Comput. 2008, pp. 589–593.
[29] X. Yang, Firefly algorithms for multimodal optimization, Int. Symp. Stoch. Algorithms 2009, pp. 169–178.
[30] A. Zengin and S. Tuncel, A survey on swarm intelligence based routing protocols in wireless sensor networks, Int. J. Phys. Sci. 5(14) (2010) 2118–2126.
[31] L. Zhang, L. Liu, X. Yang and Y. Dai, A novel hybrid firefly algorithm for global optimization, PloS One. 11(9) (2016).