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Grey Prediction Evolution Algorithm Based on Accelerated Even Grey Model
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DOI：10.1109/ACCESS.2020.3001194.png)
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摘要
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The grey prediction evolution algorithm based on the even grey model (GPEAe) is a pioneer of prediction-based evolutionary algorithms. Its offsprings are generated by a first-order inverse accumulating generation operation (1-IAGO) depending on every prediction of the even grey model. For the fact that the original values are already known for the first few predicted values in 1-IAGO, this paper firstly develops an accelerated 1-IAGO (1-AIAGO) which replaces a particular prediction with the corresponding original value. An accelerated even grey model (AEGM(1,1)) based on the 1-AIAGO is then proposed. Finally, this paper proposes a new grey prediction evolution algorithm (GPEAae) which uses the AEGM(1,1) as the reproduction operator of evolutionary algorithm to forecast the offspring. The performance of GPEAae is verified on the CEC2014 benchmark functions and the set containing nine engineering constrained design problems. The experimental results show that the GPEAae has superiority and highly competitive effectiveness when compared with the GPEAe and other state-of-the-art algorithms. The motivation of the GPEAae is using the iterative frame of evolutionary algorithms to superimpose the weak effect generated by the proposed 1-AIAGO which replaces the approximate (predicted) value with the corresponding accurate (original) value. Matlab_Codes of this article can be found in https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.
Keyword:
Predictive models
Prediction algorithms
Acceleration
Prediction theory
Data models
Approximation algorithms
Optimization
Accelerated even grey model
engineering constrained design problem
grey prediction evolution algorithm
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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