Abstract
Brain storm optimization (BSO) algorithm is a novel swarm intelligence algorithm. Inspired by differential evolution (DE) with multi-population based ensemble of mutation strategies (MPEDE), a new variant of BSO algorithm, called brain storm optimization with multi-population based ensemble of creating operations (MPEBSO), is proposed in this paper. There are three equally sized smaller indicator subpopulations and one much larger reward subpopulation. BSO algorithm is used to update individuals in every subpopulation. At first, each creating operation has one smaller indicator subpopulation, in which different mutation strategy is used to add noise instead of the Gaussian random strategy. After every certain number of generations, the larger reward subpopulation will be adaptively assigned to the best performing creating operation with more computational resources. The competitive performance of the proposed MPEBSO on CEC2005 benchmark functions is highlighted compared with DE, MPEDE, and other four variants of BSO.
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References
Shi, Y.: An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. 2, 35–62 (2011)
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
Zhan, Z., Zhang, J., Shi, Y., Liu, H.: A modified brain storm optimization. In: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, Brisbane, Australia, pp. 1969–1976 (2012)
Duan, H., Li, C.: Quantum-behaved brain storm optimization approach to solving loneys solenoid problem. IEEE Trans. Magn. 51, 1–7 (2015)
Cao, Z., Wang, L., Hei, X., Shi, Y., Rong, X.: An improved brain storm optimization with differential evolution strategy for applications of ANNs. Math. Probl. Eng. 2015, 1–18 (2015)
El-Abd, M.: Brain storm optimization algorithm with re-initialized ideas and adaptive step size. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp. 2682–2686 (2016)
El-Abd, M.: Global-best brain storm optimization algorithm. Swarm Evol. Comput. 37, 27–44 (2017)
Wu, G., Malipeddi, R., Suganthan, P.N., Wang, R., Chen, H.: Differential evolution with multi-population based ensemble of mutation strategies. J. Inf. Sci. 329, 329–345 (2016)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global. Optim. 11, 341–359 (1997)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13, 945–958 (2009)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighbourhood based mutation operator. IEEE Trans. Evol. Comput. 13, 526–553 (2009)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, KanGAL Report #2005005. IIT Kanpur, India (2005)
Acknowledgments
This research is partly supported by Humanity and Social Science Youth foundation of Ministry of Education of China (Grant No. 12YJCZH179), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 16KJA110001), the National Natural Science Foundation of China (Grant No. 11371197), the Foundation of Jiangsu Key Laboratory for NSLSCS (Grant No. 201601). The authors thank the anonymous reviewers for providing valuable comments to improve this paper, and add special thanks to Professor Mohammed El-Abd and Cao zijian for providing the source codes of the comparative algorithms (GBSO, IRGBSO, BSODE).
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Sun, Y., Jin, Y., Wang, D. (2018). Brain Storm Optimization with Multi-population Based Ensemble of Creating Operations. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_36
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DOI: https://doi.org/10.1007/978-3-319-93815-8_36
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