TY - JOUR
T1 - Computer-aided intelligent design using deep multi-objective cooperative optimization algorithm
AU - Hao, Jingwei
AU - Luo, Senlin
AU - Pan, Limin
N1 - Publisher Copyright:
© 2021
PY - 2021/11
Y1 - 2021/11
N2 - 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 multilayer perceptrons, 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.
AB - 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 multilayer perceptrons, 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.
KW - Multi-objective decision-making
KW - RBF neural network
KW - Weight-based algorithm
UR - http://www.scopus.com/inward/record.url?scp=85106451488&partnerID=8YFLogxK
U2 - 10.1016/j.future.2021.05.014
DO - 10.1016/j.future.2021.05.014
M3 - Article
AN - SCOPUS:85106451488
SN - 0167-739X
VL - 124
SP - 49
EP - 53
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -