Abstract
Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms.
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