scholarly journals Application of probability-based multi-objective optimization in material engineering

2022 ◽  
Vol 70 (1) ◽  
pp. 1-12
Author(s):  
Maosheng Zheng
Keyword(s):  
Optimal Design ◽  
Rf Mems ◽  
Material Selection ◽  
Selection Process ◽  
Compaction Pressure ◽  
Multi Objective Optimization ◽  
Optimal Scheme ◽  
Multi Objective ◽  
Material Engineering ◽  
Natural Hydroxyapatite

Introduction/purpose: Althought many methods have been proposed to deal with the problem of material selection, there are inherent defects of additive algorithms and subjective factors in such algorithms. Recently, a probability-based multi-objective optimization was developed to solve the inherent shortcomings of the previous methods, which introduces a novel concept of preferable probability to reflect the preference degree of the candidate in the optimization. In this paper, the new method is utilized to conduct an optimal scheme of the switching material of the RF-MEMS shunt capacitive switch, the sintering parameters of natural hydroxyapatite and the optimal design of the connecting claw jig. Methods: All performance utility indicators of candidate materials are divided into two groups, i.e., beneficial or unbeneficial types for the selection process; each performance utility indicator contributes quantitatively to a partial preferable probability and the product of all partial preferable probabilities makes the total preferable probability of a candidate, which transfers a multi-objective optimization problem into a single-objective optimization one and represents a uniquely decisive index in the competitive selection process. Results: Cu is the appropriate material in the material selection for RF - MEMS shunt capacitive switches; the optimal sintering parameters of natural hydroxyapatite are at 1100°C and 0 compaction pressure; and the optimal scheme is scheme No 1 for the optimal design of a connecting claw jig. Conclusion: The probability-based multi-objective optimization can be easily used to deal with an optimal problem objectively in material engineering.

scholarly journals A New "Intersection" Method for Multi-Objective Optimization in Material Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 562-568
Author(s):  
Maosheng Zheng ◽  
Yi Wang ◽  
Haipeng Teng
Keyword(s):  
Probability Theory ◽  
Set Theory ◽  
Material Property ◽  
Material Selection ◽  
Selection Process ◽  
Joint Probability ◽  
Simultaneous Optimization ◽  
Multi Objective Optimization ◽  
Evaluation Indexes ◽  
Multi Objective

Till now the previous methods for multi-objective optimization adopt the "additive" algorithm for the normalized evaluation indexes, which has the inherent shortcoming of taking the form of "union" in the viewpoint of set theory. In fact, "simultaneous optimization of multiple indexes" should be more appropriate to take the form of "intersection" for the normalized evaluation indexes in the respects of set theory and "joint probability" in probability theory. In this paper, a new concept of favorable probability is proposed to reflect the favorable degree of the candidate material in the selection; All material property indicators are divided into beneficial or unbeneficial types to the material selection; Each material property indicator correlates to a partial favorable probability quantitatively, and the total favorable probability of a candidate material is the product of all partial favorable probabilities in the viewpoints of "intersection" of set theory and "joint probability" in probability theory, which is the sole decisive index in the competitive selection process. Results of the application examples indicate the validity of the new method.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

Optimal design of bioretention cells using multi-objective optimization technique

2018 ◽  
Vol 102 ◽  
pp. 134-140 ◽  
Author(s):  
Okjeong Lee ◽  
Sangdan Kim ◽  
Jungmin Lee ◽  
Yoonkyung Park
Keyword(s):  
Optimal Design ◽  
Optimization Technique ◽  
Multi Objective Optimization ◽  
Multi Objective

A Novel Approach for the Optimal Design of a Vertical Cellular Breakwater Based on Multi-Objective Optimization

2017 ◽  
Vol 59 (4) ◽  
pp. 1750019-1-1750019-23 ◽  
Author(s):  
Lamanto T. Somervell ◽  
Santosh G. Thampi ◽  
A. P. Shashikala
Keyword(s):  
Optimal Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Novel Approach

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.

Sign in / Sign up

Export Citation Format

Share Document