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A grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation
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AI
DOI：10.1016/j.eswa.2023.121261.png)
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Abstract
En 中文
Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate.If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/ Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.
Keywords:
Engineering design problems
Grey prediction evolutionary algorithm
Meta-heuristic algorithm
Quadratic interpolation
Journal
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7.5
Papers:
2.9W
Citations:
10.2W