Abstract
The simple brain storm optimization (SimBSO) algorithm is an adjusted algorithm to simplify the process of clustering in brain storm optimization algorithm (BSO). However, SimBSO has not significantly improved the optimization performance of BSO except for its simple algorithm structure. In this paper, a new algorithm named quantum-behaved simple brain storm optimization with simplex search (QSimplex-SimBSO) is proposed to improve the performance of SimBSO. In QSimplex-SimBSO, the quantum behavior is added into SimBSO to strengthen global searching capability and then the Nelder-Mead Simplex (NMS) method is used to enhance local searching capability. After large number of experiments on the Hedar set, the results show that QSimplex-SimBSO gets a better balance of global exploration and local exploitation by the visualizing confidence interval method. Meanwhile, QSimplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions.
This work was supported by the Basic and Applied Basic Research Funding Program of Guangdong Province (Grant No. 2019A1515111097), Yunnan Provincial Research Foundation for Basic Research, China (Grant No. 202001AU070041) and Guangdong Universities’ Special Projects in Key Fields of Natural Science (No.2019KZDZX1005).
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
Similar content being viewed by others
References
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
Jiang, Y., Chen, X., Zheng, F., Niyato, D., You, X.: Brain storm optimization-based edge caching in fog radio access networks. IEEE Trans. Veh. Technol. 70(2), 1807–1820 (2021)
Duan, H., Li, S., Shi, Y.: Predator-prey brain storm optimization for DC brushless motor. IEEE Trans. Magn. 49(10), 5336–5340 (2013)
Ma, X., Jin, Y., Dong, Q.: A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Appl. Soft Comput. 54, 296–312 (2017)
Zhu, H., Shi, Y.: Brain storm optimization algorithms with k-medians clustering algorithms. In: 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), pp. 107–110. IEEE, Chiang Mai (2015)
Zhan, Z., Zhang, J., Shi, Y., Liu, H.: A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE, Brisbane (2012)
Song, Z., Peng, J., Li, C., Liu, P.X.: A simple brain storm optimization algorithm with a periodic quantum learning strategy. IEEE Access 6(19), 19968–19983 (2017)
Shi, Y.: Brain storm optimization algorithm in objective space. In: Congress on Evolutionary Computation (CEC). IEEE, Sendai (2015)
Cao, Y., et al.: A simple brain storm optimization algorithm via visualizing confidence intervals. Simul. Evol. Learn. 27–38 (2017)
Greiner, W.: Quantum mechanics. An introduction. 4th edn. J. Phys. Am. (2001)
Duan, H., Cong, L.: Quantum-behaved brain storm optimization approach to solving Loney’s solenoid problem. IEEE Trans. Magn. 51(1Pt.2), 7000,307-1–7000,307-7 (2015)
Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 61–66. IEEE, Nagoya (1996)
Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 325–331. IEEE, Portland (2004)
Zhu, K., Jiang, M., Cheng, Y.: Niche artificial fish swarm algorithm based on quantum theory. In: IEEE 10th International Conference on Signal Processing Proceedings, pp. 1425–1428. IEEE, Beijing (2010)
Niu, Q., Zhou, T., Shiwei, M.: A quantum-inspired immune algorithm for hybrid flow shop with makespan criterion. J. Univ. Comput. Sci. 15, 765–785 (2009)
Wang, L., Niu, Q., Fei, M.: A novel quantum ant colony optimization algorithm. Bio-Inspired Computat. Intell. Appl. 4688, 277–286 (2007)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313(1965)
Fan, S.-K.S., Zahara, E.: A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur. J. Oper. Res. 181(2), 527–548 (2007)
Chen, W., Cao, Y., Cheng, S., Sun, Y., Liu, Q., Li, Y.: Simplex search-based brain storm optimization. IEEE Access 6(75), 75997–76006 (2018)
Chelouah, R., Siarry, P.: Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur. J. Oper. Res. 148(2), 335–348 (2003)
Lin, H.: Hybridizing differential evolution and Nelder-Mead simplex algorithm for global optimization. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp. 198–202. IEEE, Wuxi (2016)
Chelouah, R., Siarry, P.: A hybrid method combining continuous Tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions. Eur. J. Oper. Res. 161(3), 636–654 (2005)
Dasril, Y. B., Wen, G.K.: Modified artificial bees colony algorithm with Nelder-Mead search algorithm. In: 2016 12th International Conference on Mathematics, Statistics, and Their Applications, pp.25–30. IEEE, Hatyai, Songkhla (2017)
Liu, Q., et al.: Benchmarking stochastic algorithms for global optimization problems by visualizing confidence intervals. IEEE Trans. Cybern. 47(9), 1–14 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Chen, W., Liu, Q., Cao, Y., Cheng, S., Yang, Y. (2022). Quantum-Behaved Simple Brain Storm Optimization with Simplex Search. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-09677-8_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09676-1
Online ISBN: 978-3-031-09677-8
eBook Packages: Computer ScienceComputer Science (R0)