Intelligent design generation of plane pattern based on RJMCMC algorithm

Abstract

Pattern design has a wide range of applications in daily life, such as decoration design, web design, and so on. Common design methods include artificial design and intelligent design, and design methods based on intelligent technology tend to have higher efficiency. In order to make the generation of plane pattern intelligent design more convenient and fast, a pattern design generation method based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm was proposed, and the correlation was carried out. At the same time, the same type of genetic algorithm (GA) and ant colony algorithm (AC) were introduced for performance comparison and verification. The research results show that the maximum pattern sampling accuracy of the RJMCMC algorithm is about 0.972, and the AUC value corresponding to the ROC curve is about 0.936, which are higher than other algorithms. In the algorithm iteration process, the iterative efficiency of the RJMCMC algorithm is also higher than the other two algorithms. At the same time, in the same pattern sampling processing time, the sampling number of RJMCMC algorithm is significantly higher than the other two algorithms. In the comparison of the loss value curve, the RJMCMC algorithm shows a better fitting effect, and its loss value is lower than the other two algorithms after iteration. In addition, after iteration, the error of RJMCMC algorithm is also smaller than the other two algorithms. Comprehensive analysis shows that the RJMCMC algorithm has excellent performance in pattern sampling, which can provide great help for the design and generation of plane patterns.

Keywords

iterative efficiency pattern design RJMCMC algorithm sampling quantity GA algorithm AC algorithm

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