基于遗传算法的陶瓷设计图纸智能单元布局算法

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI:10.12694/scpe.v24i3.2284

Xinqing Li, Juan Li, Qin He

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引用次数: 0

摘要

传统的陶瓷设计图元智能布局算法主要采用密度分布法获取陶瓷设计图元单元，容易受到图案密度的影响，导致设计图元分布均衡性较低。因此，需要设计一种基于遗传算法的陶瓷设计制图要素智能布局算法。即结合陶瓷设计制图元素的特点，构建陶瓷设计制图元素的智能分型结构，然后利用遗传算法设计陶瓷设计制图元素的辉煌布局模型。实验结果表明，所开发的陶瓷设计要素智能布局算法具有较高的分布平衡性，证明所设计的陶瓷设计要素智能布局算法具有良好的性能、可靠性，具有一定的应用价值，为提高陶瓷设计图纸的审美质量做出了具体贡献。

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Intelligent Element Layout Algorithm of Ceramic Design Drawing Based on Genetic Algorithm

Conventional intelligent layout algorithm of ceramic design drawing elements mainly uses density distribution method to obtain ceramic design drawing element units, which is vulnerable to the influence of pattern density, resulting in a low distribution balance of design drawing elements. Therefore, a new intelligent layout algorithm for ceramic design drawing elements needs to be designed based on a genetic algorithm. That is, combining the characteristics of ceramic design drawing elements, the intelligent parting structure of ceramic design drawing elements is constructed, and then the brilliant layout model of ceramic design drawing elements is designed using genetic algorithm. The experimental results show that the developed intelligent layout algorithm of ceramic design elements has a high distribution balance, which proves that the designed intelligent layout algorithm of ceramic design elements has good performance, reliability, and certain application value, and has made specific contributions to improving the aesthetic quality of ceramic design drawings.

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来源期刊

Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

2.00

自引率

0.00%

发文量

期刊介绍： The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.