A deep neural network with a fuzzy multi-objective optimization model for fault analysis in an elastic optical network

2022 ◽  
Vol 43 ◽  
pp. 100644
Author(s):  
André Luiz Ferraz Lourenço ◽  
Amílcar Careli César
Keyword(s):  
Neural Network ◽  
Optimization Model ◽  
Deep Neural Network ◽  
Optical Network ◽  
Fault Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Elastic Optical Network

scholarly journals New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

2019 ◽  
Vol 39 (4) ◽  
pp. 850-865
Author(s):  
Dinh-Nam Dao ◽  
Li-Xin Guo
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Optimization Problem ◽  
Mean Square Displacement ◽  
Mean Square ◽  
Stiffness Parameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Engine Mount

In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

scholarly journals Forecasting stock index with multi-objective optimization model based on optimized neural network architecture avoiding overfitting

2018 ◽  
Vol 15 (1) ◽  
pp. 211-236 ◽  
Author(s):  
Zhou Tao ◽  
Hou Muzhou ◽  
Liu Chunhui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Optimization Model ◽  
Network Architecture ◽  
Stock Index ◽  
Multi Objective Optimization ◽  
Neural Network Architecture ◽  
Training Time ◽  
Multi Objective

In this paper, the stock index time series forecasting using optimal neural networks with optimal architecture avoiding overfitting is studied. The problem of neural network architecture selection is a central problem in the application of neural network computation. After analyzing the reasons for overfitting and instability of neural networks, in order to find the optimal NNs (neural networks) architecture, we consider minimizing three objective indexes: training and testing root mean square error (RMSE) and testing error variance (TEV). Then we built a multi-objective optimization model, then converted it to single objective optimization model and proved the existence and uniqueness theorem of optimal solution. After determining the searching interval, a Multiobjective Optimization Algorithm for Optimized Neural Network Architecture Avoiding Overfitting (ONNAAO) is constructed to solve above model and forecast the time series. Some experiments with several different datasets are taken for training and forecasting. And some performance such as training time, testing RMSE and neurons, has been compared with the traditional algorithm (AR, ARMA, ordinary BP, SVM) through many numerical experiments, which fully verified the superiority, correctness and validity of the theory.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry

2021 ◽  
Vol 294 ◽  
pp. 126230
Author(s):  
Fahimeh Shafiee ◽  
Aliyeh Kazemi ◽  
Ahmad Jafarnejad Chaghooshi ◽  
Zeinab Sazvar ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Optimization Model ◽  
Production Management ◽  
Dairy Industry ◽  
Real Case ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Social Consideration
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