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改进的三维花瓶造型设计交互式遗传算法。

Improved Interactive Genetic Algorithm for Three-Dimensional Vase Modeling Design.

机构信息

School of Computer Science, Minnan Normal University, Zhangzhou 363000, China.

School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China.

出版信息

Comput Intell Neurosci. 2022 Jul 8;2022:6315674. doi: 10.1155/2022/6315674. eCollection 2022.

DOI:10.1155/2022/6315674
PMID:35845867
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9286989/
Abstract

Interactive genetic algorithm (IGA) is an effective way to help users with product design optimization. However, in this process, users need to evaluate the fitness of all individuals in each generation. It will cause users' fatigue when users cannot find satisfactory products after multi-generation evaluations. To solve this problem, an improved interactive genetic algorithm (IGA-KDTGIM) is proposed, which combines K-dimensional tree surrogate model and a graphic interaction mechanism. In this algorithm, the K-dimensional tree surrogate model is built on the basis of users' historical evaluation information to assist the user's evaluation, so as to reduce the times of users' evaluation. At the same time, users are allowed to interact with the graphic interface to adjust the shape of the individual, so as to increase users' creation fun and to make the evolution direction of the population conform to users' expectations. The IGA-KDTGIM is applied to the 3D vase design system and independently experimented with IGA, IGA-KDT, and IGA-GIM, respectively. The average fitness, maximum average fitness, and evaluation times of statistical data were compared and analyzed. Compared with traditional IGA, the number of evaluations required by users decreased by 60.0%, and the average fitness of the population increased by 15.0%. The results show that this method can reduce the users' operation fatigue and improve the ability of finding satisfactory solutions to a certain extent.

摘要

交互式遗传算法（IGA）是帮助用户进行产品设计优化的有效方法。然而，在这个过程中，用户需要评估每一代中所有个体的适应度。当用户在经过多代评估后仍无法找到满意的产品时，这会使用户感到疲劳。为了解决这个问题，提出了一种改进的交互式遗传算法（IGA-KDTGIM），该算法结合了 K 维树代理模型和图形交互机制。在该算法中，K 维树代理模型是在用户的历史评估信息的基础上建立的，以协助用户评估，从而减少用户评估的次数。同时，允许用户与图形界面进行交互，以调整个体的形状，从而增加用户的创作乐趣，并使种群的进化方向符合用户的期望。将 IGA-KDTGIM 应用于 3D 花瓶设计系统，并分别与 IGA、IGA-KDT 和 IGA-GIM 进行独立实验。比较和分析了统计数据的平均适应度、最大平均适应度和评估次数。与传统的 IGA 相比，用户所需的评估次数减少了 60.0%，种群的平均适应度提高了 15.0%。结果表明，该方法可以在一定程度上降低用户的操作疲劳，并提高寻找满意解决方案的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/237bdeab30b0/CIN2022-6315674.alg.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/237bdeab30b0/CIN2022-6315674.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/1d127c693354/CIN2022-6315674.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/b36f6b1df117/CIN2022-6315674.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/e3c783760771/CIN2022-6315674.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/302fd3e2e6fc/CIN2022-6315674.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/13c5518062b0/CIN2022-6315674.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1037/9286989/39fa889a6b42/CIN2022-6315674.011.jpg
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