This book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection systematically, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies, such as response surface methodology, orthogonal experimental design, and uniform experimental design, are all performed; the conditions of the material performance utility with desirable value and robust assessment are included; the discretization treatment of complicated integral in the evaluation is presented.
The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone.
This book can be used as a textbook for postgraduate and advanced undergraduate students in material relevant majors, and a reference book for scientists and engineers digging in the related fields.
Author(s): Maosheng Zheng, Haipeng Teng, Jie Yu, Ying Cui, Yi Wang
Publisher: Springer
Year: 2022
Language: English
Pages: 155
City: Singapore
Preface
Contents
About the Authors
1 History and Current Status of Material Selection with Multi-objective Optimization
1.1 Brief Introduction
1.2 Evolution of Material Selections
1.3 Evolution of Multi-objective Optimization
1.4 Summary and Conclusions
References
2 Introduction to Multi-objective Optimization in Material Selections
2.1 Introduction
2.2 Previous Methodologies for Multiple Objective Optimization of Material Selection
2.2.1 Qualitative Methodology
2.2.2 Quantitative Methodology
2.2.3 Discussion and Summary of the Previous Methodology for Multiple Objective Optimization of Material Selection
2.3 Fundamental Consideration of Multiple-objective Optimization for Material Selection
2.3.1 Statement of Situation
2.3.2 Basic Procedure for Material Selection
References
3 Fundamental Principle of Probability-Based Multi-objective Optimization and Applications
3.1 Introduction
3.2 Arithmetic of Probability Treatment
3.3 Quantitative Approach for Material Selection on Basis of Probability Theory
3.3.1 Concept of Preferable Probability
3.3.2 Probability Based Approach
3.4 Applications of the Probability-Based Method for Multi-objective Optimization in Material Selection
3.5 Other Applications in More Broader and General Issues
3.6 Concluding Remarks
References
4 Extension of Probability-Based Multi-objective Optimization in Condition of the Utility with Interval Number
4.1 Introduction
4.2 Extension of Probability-Based Multi-objective Optimization Involving Robustness
4.3 Application of the Extended PMOO in Evaluation of Optimal Problems with Variance of Data in Material Engineering
4.4 Conclusion
References
5 Extension of Probability-Based Multi-objective Optimization in Condition of the Utility with Desirable Value
5.1 Introduction
5.2 Assessments of Partial and Overall Preferable Probability for Performance Response with Desirable Value in the Probability-Based Multiple Objectives Optimization
5.2.1 One Range Desirable Value Problem
5.2.2 One Side Desirable Value Problem
5.3 Applications
5.4 Optimization of Maximizing Conversion Rate with Constraints of Desirable Thermal Activity
5.5 Concluding Remarks
References
6 Hybrids of Probability-Based Multi-objective Optimization with Experimental Design Methodologies
6.1 Introduction
6.2 Hybrid of Probability-Based Multi-objective Optimization with Orthogonal Experimental Design
6.2.1 Algorithm of the Hybrid for PMOO with Orthogonal Experimental Design
6.2.2 Application of the Hybrid of PMOO with Orthogonal Experimental Design in Material Selection
6.3 Hybrid of Probability Based Multi-objective Optimization with Response Surface Methodology Design
6.3.1 Algorithm of the Hybrid for PMOO with Response Surface Methodology (RSM)
6.4 Hybrid of Probability Based Multi-objective Optimization with Uniform Experimental Design Methodology
6.4.1 Algorithm of the Hybrid for PMOO with Uniform Experimental Design Methodology (UED)
6.4.2 Application of the Hybrid of PMOO with Uniform Experimental Design Methodology in Material Selection
6.5 Conclusion
References
7 Discretization of Complicated Integral in Assessing Probability-Based Multi-objective Optimization by Means of GLP and Uniform Experimental Design
7.1 Introduction
7.2 Fundamental Characteristic of Uniform Experimental Design
7.2.1 Main Features of Uniform Experimental Design
7.2.2 Fundamental Principle of Uniform Experimental Design
7.3 Feature Analysis of the Periodic Function in a Single Period
7.4 Typical Examples for the Efficient Approach of Numerical Integration for a Single Peak Function Based on Rules of GLD and Uniform Design Method
7.5 Typical Examples of Applications of the Finite Sampling Point Method in Assessment of Probability-Based Multi-objective Optimization
7.6 Conclusive Remarks
References
8 Applications of Probability-Based Multi-objective Optimization Beyond Material Selection
8.1 Introduction
8.2 Application of the Multi-objective Optimization in Drug Design and Extraction
8.2.1 Optimal Preparation of Encapsulation Composite of Water-Soluble Chitosan/poly-Gama-Glutamic Acid-Tanshinone IIA with Response Surface Methodology Design
8.2.2 Optimal Preparation of Glycerosomes-Triptolide as an Encapsulation Composite with Orthogonal Experimental Design
8.2.3 Optimization of Compatibility of the Traditional Chinese Medicine Drug by Using Orthogonal Experimental Design
8.2.4 Optimization of Multi-objective Drug Extraction Conditions Based on Uniform Experimental Designs
8.3 Application of the Probability Based Multi-objective Optimization in Military Engineering Project with Weighting Factor
8.3.1 Decision Making of Multi-objective Military Engineering Investment
8.3.2 Flexible Ability Assessment of Antiaircraft Weapon System
8.4 Comparative Analysis of Scheme Selection for Water Purification Treatment by Using PMOO with the Traditional MCDM
8.5 Application of the Probability-Based Multi-objective Optimization in Power Equipment
8.6 Conclusion
References
9 General Conclusions
10 Afterword
10.1 On Preferable Probability
10.2 On the Utility with Interval Value and Robust Assessment
10.3 On the Number of Discretized Sampling Points with Characteristic of GLP for Assessing Complicated Integral
10.4 Hybrid of Probability–Based Multi–Objective Optimization with Sequential Uniform Design
References
