What is it about?
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.
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Why is it important?
Our research puts forward a new idea of intelligent algorithm improvement, and puts forward a new intelligent optimization algorithm. The performance advantages of the new algorithm are also tested in many aspects.
Perspectives
I hope that the continuous development of evolutionary algorithms can help other industrial applications make continuous progress and continuously improve efficiency and production capacity.
Zenglin QIAO
Beijing University of Posts and Telecommunications
Read the Original
This page is a summary of: Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns, International Journal of Intelligent Systems, October 2021, Wiley,
DOI: 10.1002/int.22658.
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