Abstract
This paper proposes a mutational Multi-Verse Optimizer (MVO) algorithm based on Lévy flight and called LMVO algorithm. The random steps of Lévy flight enhances the ability of the search individual to escape the local optimum, and promotes the balance of exploration and exploitation for MVO algorithm. For investigate the availability of LMVO, add basic MVO algorithm and other four mainstream algorithms to compare with it on six high dimensional test functions and two fixed-dimensional test functions. Furthermore, apply it to cantilever beam design problem. These final results proved that LMVO has good convergence accuracy and stability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)
Faris, H., Aljarah, I., Mirjalili, S.: Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl. Intell. 45(2), 1–11 (2016)
Ali, E.E., El-Hameed, M.A., El-Fergany, A.A., El-Arini, M.M.: Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustain. Energy Technol. Assess. 17, 68–76 (2016)
Fathy, A., Rezk, H.: Multi-verse optimizer for identifying the optimal parameters of PEMFC model. Energy 143, 634–644 (2018)
Karthikeyan, K., Dhal, P.K., Karthikeyan, K., Dhal, P.K.: Multi verse optimization (MVO) technique based voltage stability analysis through continuation power flow in IEEE 57 bus. Energy Procedia 117, 583–591 (2017)
Faris, H., Hassonah, M.A., Al-Zoubi, A.M., Mirjalili, S., Aljarah, I.: A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput. Appl., 1–15 (2017)
Ewees, A.A., Aziz, M.A.E., Hassanien, A.E.: Chaotic multi-verse optimizer-based feature selection. Neural Comput. Appl. 1, 1–16 (2017)
Jangir, P., Parmar, S.A., Trivedi, I.N., Bhesdadiya, R.H.: A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng. Sci. Technol. Int. J. 20(2), 570–586 (2016)
Mirjalili, S., Jangir, P., Mirjalili, S.Z., Saremi, S., Trivedi, I.N.: Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl. Based Syst. 134, 50–71 (2017)
Viswanathan, G.M., Afanasyev, V., Buldyrev, S.V., Murphy, E.J., Prince, P.A., Stanley, H.E.: Lévy flight search patterns of wandering albatrosses. Nature 381(6581), 413–415 (1996)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, vol. 71, pp. 210–214. IEEE (2010)
Haklı, H., Uğuz, H.: A novel particle swarm optimization algorithm with levy flight. Appl. Soft Comput. J. 23(5), 333–345 (2014)
Kalantzis, G., Shang, C., Lei, Y., Leventouri, T.: Investigations of a gpu-based levy-firefly algorithm for constrained optimization of radiation therapy treatment planning. Swarm Evol. Comput. 26, 191–201 (2016)
Hussein, W.A., Sahran, S., Abdullah, S.N.H.S.: Patch-levy-based initialization algorithm for bees algorithm. Appl. Soft Comput. J. 23(5), 104–121 (2014)
Chickermane, H., Gea, H.C.: Structural optimization using a new local approximation method. Int. J. Numer. Meth. Eng. 39(5), 829–846 (2015)
Coello, C.A.C.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41, 113–127 (2000)
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(2), 245 (2013)
Cheng, M.Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)
Acknowledgment
This work is supported by Innovation Project of Guangxi Graduate Education under Grant No. gxun-chxzs2017135.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Liu, J., He, D. (2018). An Mutational Multi-Verse Optimizer with Lévy Flight. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_84
Download citation
DOI: https://doi.org/10.1007/978-3-319-95930-6_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95929-0
Online ISBN: 978-3-319-95930-6
eBook Packages: Computer ScienceComputer Science (R0)