Abstract
Dynamic multimodal optimization (DMO) problem is introduced and solved with brain storm optimization (BSO) algorithms in this paper. A dynamic multimodal optimization problem is defined as an optimization problem with multiple global optima and characteristics of global optima are changed during the search process. The effectiveness of BSO algorithm is validated on a test problem which was constructed based on the dynamic optimization and multimodal optimization. Results show that BSO algorithm is an efficient and robust optimization method for solving dynamic multimodal optimization problems.
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
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)
Wang, Y., Dang, C.: An evolutionary algorithm for dynamic multi-objective optimization. Appl. Math. Comput. 205(1), 6–18 (2008)
Parrott, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)
Rönkkönen, J.: Continuous multimodal global optimization with differential evolution-based methods. Department of information technology, Lappeenranta University of Technology (2009)
Li, X.: Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans. Evol. Comput. 14(1), 150–169 (2010)
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). https://doi.org/10.1007/978-3-642-21515-5_36
Shi, Y.: An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. 2(4), 35–62 (2011)
Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46(4), 445–458 (2016)
Shi, Y.: Brain storm optimization algorithm in objective space. In: Proceedings of 2015 IEEE Congress on Evolutionary Computation, Sendai, Japan, pp. 1227–1234 (2015)
Cheng, S., et al.: A comprehensive survey of brain storm optimization algorithms. In: Proceedings of 2017 IEEE Congress on Evolutionary Computation, Donostia, San Sebastián, Spain, pp. 1637–1644 (2017)
Song, Z., Peng, J., Li, C., Liu, P.X.: A simple brain storm optimization algorithm with a periodic quantum learning strategy. IEEE Access 6, 19968–19983 (2017)
Cheng, S., Shi, Y., Qin, Q., Gao, S.: Solution clustering analysis in brain storm optimization algorithm. In: Proceedings of The 2013 IEEE Symposium on Swarm Intelligence, Singapore, pp. 111–118 (2013)
Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 13(1), 103–127 (2009)
Jiang, S., Yang, S.: Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons. IEEE Trans. Cybern. 47(1), 198–211 (2017)
Cheng, S., Chen, J., Lei, X., Shi, Y.: Locating multiple optima via developmental swarm intelligence. IEEE Access 6, 17039–17049 (2018)
Qu, B.Y., Liang, J., Suganthan, P.: Niching particle swarm optimization with local search for multi-modal optimization. Inf. Sci. 197, 131–143 (2012)
Qu, B.Y., Suganthan, P., Liang, J.: Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans. Evol. Comput. 16(5), 601–614 (2012)
Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for CEC 2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation and Machine Learning Group, RMIT University (2013)
Burke, E.K., Hyde, M.R., Kendall, G.: Providing a memory mechanism to enhance the evolutionary design of heuristics. In: Proceedings of 2010 IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Shi, Y.: Developmental swarm intelligence: developmental learning perspective of swarm intelligence algorithms. Int. J. Swarm Intell. Res. 5(1), 36–54 (2014)
Shi, Y.: Unified swarm intelligence algorithms. In: Shi, Y. (ed.) Critical Developments and Applications of Swarm Intelligence, pp. 1–26 (2018)
Acknowledgments
This work was jointly supported by National Natural Science Foundation of China (No. 61671041, 61773119, 61771297, and 61703256), and the Fundamental Research Funds for the Central Universities under Grant GK201703062.
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
Cheng, S., Lu, H., Song, W., Chen, J., Shi, Y. (2018). Dynamic Multimodal Optimization Using Brain Storm Optimization Algorithms. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_21
Download citation
DOI: https://doi.org/10.1007/978-981-13-2826-8_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2825-1
Online ISBN: 978-981-13-2826-8
eBook Packages: Computer ScienceComputer Science (R0)