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Gaussian bare-bones gradient-based optimization: Towards mitigating the performance concerns
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DOI：10.1002/int.22658.png)
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Abstract
En 中文
Gradient-based optimizer (GBO) is a metaphor-free mathematic-based algorithm proposed in recent years. Encouraged by the gradient-based Newton's method, this algorithm combines with population-based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare-bones mechanism, an opposition-based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO-KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO-KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO-KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to for any guidance about the proposed GOMGBO method.
Keywords:
global optimization
gradient-based optimizer
kernel extreme learning machine
medical diagnosis
parameter optimization
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