Abstract
Multifactorial optimization (MFO) is an optimization problem proposed in recent years to solve the multiple problems simultaneously. In this article we will introduce brain storm optimization (BSO) algorithm into MFO, and name this new methodology as multi-factorial brain storm optimization algorithm (MFBSA). In addition, we propose a new strategy of applying clustering technique into multitasking. The clustering process gathers the tasks who have similar information into a class, promoting the solving process of these tasks. The individuals in MFBSA have different cultural and biological characteristics, and their interaction in the evolutionary process format the ways of the exchange and sharing of information between multiple tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Glob. Optim. 31(4), 635–672 (2005)
Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)
Cloninger, C.R., Rice, J., Reich, T.: Multifactorial inheritance with cultural transmission and assortative mating. II. A general model of combined polygenic and cultural inheritance. Am. J. Hum. Genet. 31(2), 176–198 (1979)
Dawkin, R.: The Selfish Gene. Oxford University Press, London (1976)
Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)
Li, Y.L., Zhan, Z.H., Gong, Y.J., Chen, W.N., Zhang, J., Li, Y.: Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Trans. Cybern. 45(9), 1798–1810 (2015)
Mills, R., Jansen, T., Watson, R.A.: Transforming evolutionary search into higher-level evolutionary search by capturing problem structure. IEEE Trans. Evol. Comput. 18(5), 628–642 (2014)
Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21515-5_36
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61603299)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zheng, X., Lei, Y., Gong, M., Tang, Z. (2016). Multifactorial Brain Storm Optimization Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-3614-9_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3613-2
Online ISBN: 978-981-10-3614-9
eBook Packages: Computer ScienceComputer Science (R0)