Probability-Based Multi-objective Optimization for Material Selection

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