.png){kind=icon}
返回
Computer-aided intelligent design using deep multi-objective cooperative optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2021.05.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Computer-aided product design means using artificial intelligent systems to automatically design multiple industrial products. This technique has been pervasively applied in multiple domains, such as 3D printing and vehicle manufacture. One challenge of computer-aided design is to incorporate deep neural network to optimally fuse multiple decisions. Multi-objective decision encapsulates many decision-making objectives and leverages deep CNNs to evaluate/optimize the fused multiple decisions. Due to the objectives of economic and social benefit, it is necessary to use a variety of criteria to deeply evaluate and optimize schemes. In this paper, we propose a novel quality-guided deep neural network and weighting scheme to achieve multi-objective decision. We leverage RBF neural network to construct objective weight assignment model. Then, a deep CNN is designed to implement the weighting task, each of which corresponds to a single decision. Our deep CNN has five layers and contains multilayerperceptrons, which indicate the fully connected networks. Each neuron in one layer is connected to all neurons in the next layer. The target of our deep weight-based model is that the multi-objective optimization can be formulated as a single-objective optimization by assigning different weights to each objective. Finally, the non-inferior solution of the multi-objective optimization is generated by updating the weights of the deep CNN during fine tuning. In our experiment, we have demonstrated that our method has the potential to facilitate a variety of applications, such as 3D reconstruction and system optimization. We believe that our proposed algorithm can guide the optimization of various intelligent system pipeline. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective decision-making
RBF neural network
Weight-based algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏分享收藏
查看更多内容