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.

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.

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.

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

scholarly journals Handling Constrained Multi-objective Optimization By Ignoring Constraints and Using Two Evolutionary Frameworks

2020 ◽  
Author(s):  
Xiang Yi ◽  
Xiaowei Yang ◽  
Han Huang ◽  
Jiahai Wang
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Inequality Constraints ◽  
Simultaneous Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Real World Problem ◽  
Conflicting Objectives ◽  
Two Stages

Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well,constraint satisfaction is emphasized over objective minimization.Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems.

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.

scholarly journals Green Supplier Selection in the Agro-Food Industry with Contract Farming: A Multi-Objective Optimization Approach

2019 ◽  
Vol 11 (24) ◽  
pp. 7017 ◽  
Author(s):  
Marco A. Miranda-Ackerman ◽  
Catherine Azzaro-Pantel ◽  
Alberto A. Aguilar-Lasserre ◽  
Alfredo Bueno-Solano ◽  
Karina C. Arredondo-Soto
Keyword(s):  
Environmental Impact ◽  
Supplier Selection ◽  
Selection Process ◽  
Environmental Benefits ◽  
Raw Material ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Contract Farming ◽  
Green Supplier Selection ◽  
Multi Objective

An important contribution to the environmental impact of agro-food supply chains is related to the agricultural technology and practices used in the fields during raw material production. This problem can be framed from the point of view of the Focal Company (FC) as a raw material Green Supplier Selection Problem (GSSP). This paper describes an extension of the GSSP methodology that integrates life cycle assessment, environmental collaborations, and contract farming in order to gain social and environmental benefits. In this approach, risk and gains are shared by both parties, as well as information related to agricultural practices through which the FC can optimize global performance by deciding which suppliers to contract, capacity and which practices to use at each supplying field in order to optimize economic performance and environmental impact. The FC provides the knowledge and technology needed by the supplier to reach these objectives via a contract farming scheme. A case study is developed in order to illustrate and a step-by-step methodology is described. A multi-objective optimization strategy based on Genetic Algorithms linked to a MCDM approach to the solution selection step is proposed. Scenarios of optimization of the selection process are studied to demonstrate the potential improvement gains in performance.

Design of Lightweight Hybrid Materials for the Cab Body-In-White of a Commercial Vehicle Based on Multi-Objective Optimization

2013 ◽  
Vol 631-632 ◽  
pp. 281-286 ◽  
Author(s):  
An Cui ◽  
Li Juan Xu ◽  
Xian Bo Zhang ◽  
Qiang Yang
Keyword(s):  
Hybrid Materials ◽  
Total Mass ◽  
Commercial Vehicle ◽  
Material Selection ◽  
Dynamic Performance ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Lightweight Materials ◽  
Multi Objective ◽  
Body In White

In this paper, the lightweight hybrid materials cab BIW of a commercial vehicle is investigated for material selection. In order to improve the dynamic performance and reduce total mass, the lightweight materials are employed in the BIW. Nine groups of sensitive components are selected to apply the lightweight materials. The design problem is formulated as a multi-objective nonlinear programming problem and the modified non-dominated sorting genetic algorithm (NSGA-II) is used to solve the problem. The surrogate model based on radial basis function (RBF) is adopted in the optimization. The research results show that the usage of steel-aluminum hybrid materials can reduce the total mass and increase the first torsion modal frequency of the cab BIW.

Fundamental Concepts

2019 ◽  
pp. 27-77
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Probability Distributions ◽  
Possibility Theory ◽  
Multi Objective ◽  
Basic Ideas ◽  
Fuzzy Stochastic Programming

In this chapter, the authors discuss some basic concepts of probability theory and possibility theory that are useful when reading the subsequent chapters of this book. The multi-objective fuzzy stochastic programming models developed in this book are based on the concepts of advanced topics in fuzzy set theory and fuzzy random variables (FRVs). Therefore, for better understanding of these advanced areas, the authors at first presented some basic ideas of probability theory and probability density functions of different continuous probability distributions. Afterwards, the necessity of the introduction of the concept of fuzzy set theory, some important terms related to fuzzy set theory are discussed. Different defuzzification methodologies of fuzzy numbers (FNs) that are useful in solving the mathematical models in imprecisely defined decision-making environments are explored. The concept of using FRVs in decision-making contexts is defined. Finally, the development of different forms of fuzzy goal programming (FGP) techniques for solving multi-objective decision-making (MODM) problems is underlined.

A Multi-Objective Robust Preference Genetic Algorithm Based on Decision Variable Perturbation

2011 ◽  
Vol 211-212 ◽  
pp. 818-822 ◽  
Author(s):  
Bing Kun Zhu ◽  
Li Hong Xu ◽  
Hai Gen Hu
Keyword(s):  
Fitness Function ◽  
Latin Hypercube Sampling ◽  
Simultaneous Optimization ◽  
Function Parameter ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Robust Solution ◽  
Robust Solutions ◽  
Multi Objective ◽  
Conflicting Objectives

Multi-objective optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. A new robust preference multi-objective optimization algorithm is proposed in this paper, and the robust measurement of solution is designed based on Latin Hypercube Sampling, which is embedded in the optimization process to guide the optimization direction and help the better robust solution have more chance to survive. In order to obtain different preference of the robust solutions, a new fitness scheme is also presented. Through the adjustment of fitness function parameter preference, robust solutions can be obtained. Results suggest that the proposed algorithm has a bias towards the region where the preference robust solutions lie.

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