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).
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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
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