Abstract
Aiming at the shortcomings of spotted hyena optimizer, such as slow convergence speed, low searching accuracy and easy to fall into local optimal, this paper proposes an improved spotted hyena optimization algorithm (OWSHO) integrating multiple strategies. The reverse population is constructed by using the Opposition-Based Learning strategy to increase the diversity of the population and further improve the convergence speed of the algorithm. At the same time, the spiral exploration mechanism of whale optimization algorithm is combined to enhance the ability of exploring unknown regions and improve the global search performance of the algorithm. Then adaptive weight strategy is introduced to balance and improve the global exploration and local development ability of the algorithm. In this paper, 8 benchmark functions of CEC test set are used for simulation experiments, and compared with 3 heuristic algorithms, the test results show that: The improved spotted hyena optimization algorithm based on the combination of reverse learning, spiral exploration mechanism and adaptive weight strategy has a great improvement in the search accuracy and convergence speed, and gets rid of the local optimal to a certain extent, which proves the effectiveness and advancement of the algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dhiman, G., Kumar, V.: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017)
Ilany, A., Booms, A.S., Holekamp, K.E.: Topological effects of network structure on long-term social network dynamics in a wild mammal. Ecol. Lett. 18(7), 687–695 (2015)
Dhiman, G., Kumar, V.: Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl.-Based Syst. 150, 175–197 (2018)
Zhou, G., Li, J., Tang, Z., et al.: An improved spotted hyena optimizer for PID parameters in an AVR system. Math. Biosci. Eng. 17(4), 3767–3783 (2020)
Panda, N., Majhi, S.K., Singh, S., et al.: Oppositional spotted hyena optimizer with mutation operator for global optimization and application in training wavelet neural network. J. Intell. Fuzzy Syst. 38(5), 6677–6690 (2020)
Jia, H., Jiang, Z., Li, Y., et al.: Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm. J. Comput. Appl. 41(05), 1290–1298 (2021)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Jia, H., Jiang, Z., Li, Y., et al.: Feature selection based on simulated annealing spotted hyena optimization algorithm. Appl. Sci. Technol. 47(01), 74–79 (2020)
Luo, Q., Li, J., Zhou, Y.Q., et al.: Using spotted hyena optimizer for training feedforward neural networks. Cogn. Syst. Res. 65, 1–16 (2020)
Panda, N., Majhi, S.K.: Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network. Comput. Intell. 36(1), 320–350 (2020)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 1, pp. 695–701. IEEE (2005)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Dhiman, G., Kaur, A.: Spotted hyena optimizer for solving engineering design problems. In: 2017 International Conference on Machine Learning and Data Science (MLDS), pp. 114–119 (2017)
Chen, H., Li, W., Yang, X.: A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems. Expert Syst. Appl. 158, 113612 (2020)
Xiao-long, H., Gang, Z., Yue-hua, C., et al.: Multi-class algorithm of WOA-SVM using Levy flight and elite opposition-based learning. Appl. Res. Comput. 38(12), 3640–3645 (2021)
Yin, D., Zhang, D., Cai, P., et al.: improved sparrows search optimization algorithm and its application. Comput. Eng. Sci. 1–8 (2022)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Kumar, V., Kaleka, K., Kaur, A.: Spiral-inspired spotted hyena optimizer and its application to constraint engineering problems. Wirel. Pers. Commun. 116(1), 865–881 (2021)
Liu, L., Fu, S., Huang, H., et al.: A grey wolf optimization algorithm based on drunkard strolling and reverse learning. Comput. Eng. Sci. 43(09), 1558–1566 (2021)
Zhang, X., Zhang, Y., Liu, L., et al.: Improved sparrow search algorithm fused with multiple strategies. Appl. Res. Comput. 39(04), 1086–1091+1117 (2022)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS 1995. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mo, C., Wang, X., Zhang, L. (2022). Improved Spotted Hyena Optimizer Fused with Multiple Strategies. In: Cai, Z., Chen, Y., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2022. Communications in Computer and Information Science, vol 1693. Springer, Singapore. https://doi.org/10.1007/978-981-19-8152-4_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-8152-4_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8151-7
Online ISBN: 978-981-19-8152-4
eBook Packages: Computer ScienceComputer Science (R0)