scholarly journals Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1184
Author(s):  
Geraldine Cáceres Sepulveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Neural Networks ◽  
Acrylic Acid ◽  
Performance Criteria ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Meta Model ◽  
Multi Objective ◽  
Pareto Domain ◽  
Computationally Intensive ◽  
Time Required

It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

Modeling and multi-objective optimization of square cyclones using CFD and neural networks

2011 ◽  
Vol 89 (3) ◽  
pp. 301-309 ◽  
Author(s):  
H. Safikhani ◽  
M.A. Akhavan-Behabadi ◽  
N. Nariman-Zadeh ◽  
M.J. Mahmood Abadi
Keyword(s):  
Neural Networks ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 260
Author(s):  
Naomi Simumba ◽  
Suguru Okami ◽  
Akira Kodaka ◽  
Naohiko Kohtake
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Credit Scoring ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Base Classifier ◽  
Grasshopper Optimization Algorithm ◽  
Specific Implementation ◽  
The Impact

Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.

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