Abstract
LabVIEW is a versatile tool with various inbuilt toolkits to perform various measurement and control tasks. Hence, it is used in almost every field of engineering. However, it does not provide enough contribution in the field of optimization which is the major concern. It has only one optimizer based on differential evolution (DE) algorithm. Even though DE is a very effective global optimization technique, but its performance highly depends on parametric settings. DE contains high number of user-defined parameters; therefore, it becomes cumbersome for user to obtain best parametric settings for a given optimization problem. Recently, several nature-inspired algorithms are developed with reduced number of parametric settings to obtain the optimum solutions while solving complex black box optimization problems. Hence, to update the LabVIEW in the field of optimization, there exists a need of continuous development of other efficient global optimizers. Multi-verse optimizer (MVO) is considered as one of the latest but effective nature-inspired optimization algorithm with only two user-defined parameters. In this paper, MVO toolkit is developed for LabVIEW platflorm and the efficiency of the proposed toolkit is validated on a test bed of five standard benchmark functions. The statistical analysis of results shows that the MVO is far better in solving optimization problems as compared to DE.
References
Storn, R. and Price, K., 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), pp. 341–359 (1997).
Gupta, P., Rana, K. P.S., Kumar, V., Mishra, P., Kumar, J. and Nair, S.S., Development of a Grey Wolf Optimizer Toolkit in LabVIEW™. In IEEE International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), pp. 107–113 (2015).
Mishra, P., Kumar, V., Rana, K.P.S., Nair, S.S. and Kumar, J., Cuckoo search implementation in LabVIEW. In IEEE International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), pp. 331–336 (2016).
Thakur, K.S., Kumar, V., Rana, K.P.S., Mishra, P., Kumar, J. and Nair, S.S., Development of Bat Algorithm Toolkit in LabVIEW™. In IEEE International Conference on Computing, Communication & Automation (ICCCA), pp. 5–10 (2015).
Gupta, S., Kumar, V., Rana, K.P.S., Mishra, P. and Kumar, J., Development of Ant Lion Optimizer toolkit in LabVIEW™. In IEEE International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), pp. 251–256 (2016).
Varaee, H. and Ghasemi, M.R., Engineering optimization based on ideal gas molecular movement algorithm. Engineering with Computers, 33 (1), pp. 71–93 (2017).
Moez, H., Kaveh, A. and Taghizadieh, N., Natural Forest Regeneration Algorithm: A New Meta-Heuristic. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(4), pp. 311–326 (2016).
Heidari, A.A., Abbaspour, R.A. and Jordehi, A.R., An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing and Applications, 28 (1), pp. 57–85 (2017).
Wang, G.G., Deb, S., Zhao, X. and Cui, Z., A new monarch butterfly optimization with an improved crossover operator. Operational Research, pp. 1–25 (2016).
Wang, G.G., Gandomi, A.H., Alavi, A.H. and Deb, S., A multi-stage krill herd algorithm for global numerical optimization. International Journal on Artificial Intelligence Tools, 25 (02), p. 1550030 (2016).
Mirjalili, S., Mirjalili, S.M. and Hatamlou, A., Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27 (2), pp. 495–513 (2016).
Wangchamhan, T., Chiewchanwattana, S. and Sunat, K., Multilevel thresholding selection based on chaotic multi-verse optimization for image segmentation. In 13th IEEE International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6 (2016).
Ali, E.E., El-Hameed, M.A., El-Fergany, A.A. and El-Arini, M.M., Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustainable Energy Technologies and Assessments, 17, pp. 68–76 (2016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vivek, K., Deepak, M., Chetna, Mohit, J., Asha, R., Vijander, S. (2018). Development of Multi-verse Optimizer (MVO) for LabVIEW. In: Singh, R., Choudhury, S., Gehlot, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_75
Download citation
DOI: https://doi.org/10.1007/978-981-10-5903-2_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5902-5
Online ISBN: 978-981-10-5903-2
eBook Packages: EngineeringEngineering (R0)