An optimized swarm intelligence algorithm based on the mass defence of bees

Document Type : Research Paper


1 Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

2 Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran


Swarm intelligence is a modern optimization technique and one of the most efficient techniques for solving optimization problems. Their main inspiration is the cooperative behavior of animals within specific communities.  In swarm intelligence algorithms, agents work together and the collective behavior of all agent causes converge at a point close to the global optimal solution. In this paper, we model the behavior of bees in defending the hive against invading bees to provide a new optimization algorithm. In the proposed algorithm, the coordinated performance of bees in identifying the invader creating a circle around the invading bee and generating heat during the siege of the invading bee and also the heat emitted from each bee are modeled. The simulation results of the proposed algorithm show a successful competitive behavior in achieving the global optimum in comparison with the firefly, ant colony, artificial bee colony, whale and grey wolf algorithms.


[1] M. Arca, A. Papachristoforou, F. Mougel, A. Rortais, K. Monceau, O. Bonnard, P. Tardy, D. Thi´ery, 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)
[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)
[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.
[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)
[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)
[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)
Volume 13, Issue 1
March 2022
Pages 3451-3462
  • Receive Date: 08 October 2021
  • Revise Date: 14 November 2021
  • Accept Date: 19 December 2021
  • First Publish Date: 26 January 2022