Abstract
Brain storm optimization (BSO) is a relatively new swarm intelligence algorithm, which simulates the problem-solving process of human brainstorming. In General, BSO employs flat clustering which has a number of drawbacks. In this paper, the agglomerative hierarchical clustering is introduced into BSO and its impact on the performance of the creating operator is then analyzed. The proposed algorithm is applied to numerical optimization problems in comparison with the BSO with k-means Clustering. Experimental results show that the proposed algorithm achieves satisfactory results and guarantees a high coverage rate.
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References
Chen, J., Cheng, S., Chen, Y., Xie, Y., Shi, Y.: Enhanced brain storm optimization algorithm for wireless sensor networks deployment. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI-CCI 2015. LNCS, vol. 9140, pp. 373–381. Springer, Heidelberg (2015)
Chen, J., Xie, Y., Ni, J.: Brain storm optimization model based on uncertainty information. In: 2014 Tenth International Conference on Computational Intelligence and Security, pp. 99–103, November 2014
Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. (2016) (in press)
Duan, H., Li, C.: Quantum-behaved brain storm optimization approach to solving loney’s solenoid problem. IEEE Trans. Magn. 51(1), 1–7 (2015)
Jadhav, H., Sharma, U., Patel, J., Roy, R.: Brain storm optimization algorithm based economic dispatch considering wind power. In: Proceedings of the 2012 IEEE International Conference on Power and Energy (PECon 2012), Kota Kinabalu, Malaysia, pp. 588–593. December 2012
Jordehi, A.R.: Brainstorm optimisation algorithm (BSOA): an efficient algorithm for finding optimal location and setting of facts devices in electric power systems. Electr. Power Energy Syst. 69, 48–57 (2015)
Li, J., Duan, H.: Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system. Aerosp. Sci. Technol. 42, 187–195 (2015)
Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011)
Shi, Y.: An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. (IJSIR) 2(4), 35–62 (2011)
Shi, Y.: Developmental swarm intelligence: developmental learning perspective of swarm intelligence algorithms. Int. J. Swarm Intell. Res. (IJSIR) 5(1), 36–54 (2014)
Sun, Y.: A hybrid approach by integrating brain storm optimization algorithm with grey neural network for stock index forecasting. Abstr. Appl. Anal. 2014, 1–10 (2014)
Zhan, Z.h., Zhang, J., Shi, Y.h., Liu, H.l.: A modified brain storm optimization. In: Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC 2012), pp. 1–8, June 2012
Acknowledgments
The research work reported in this paper was partially supported by the National Natural Science Foundation of China under Grant Number 61273367 and 61403121 and the Fundamental Research Funds for the Central Universities under Grant Number 2015B20214.
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Chen, J., Wang, J., Cheng, S., Shi, Y. (2016). Brain Storm Optimization with Agglomerative Hierarchical Clustering Analysis. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_12
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DOI: https://doi.org/10.1007/978-3-319-41009-8_12
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