The Parameter Space Investigation Method Toolkit -- Contents -- Acknowledgments -- Preface -- Part I The Parameter Space Investigation Method Toolkit -- 1 Introduction -- 1.1 Some Basic Features of Real-Life Optimization Problems -- 1.2 Generalized Formulation of Multicriteria Optimization Problems -- 1.2.1 Definition -- 1.3 Applying Single-Criterion Methods for Solving Multicriteria Problems -- 1.3.1 Substitution of a Multitude of Criteria by a Single One -- 1.3.2 Optimization of the Most Important Criterion -- 1.4 Systematic Search in Multidimensional Domains by Using Uniformly Distributed Sequences -- 1.4.1 Quantitative Characteristics of Uniformity -- References -- 2 Parameter Space Investigation Method as a Tool for Formulation and Solution of Real-Life Problems -- 2.1 The Parameter Space Investigation Method -- 2.1.1 The Complexity of the Investigation -- 2.1.2 Definition of the Feasible Solution in Parallel Mode -- 2.1.3 Number Generators for Systematic Search in the Design Variable Space -- 2.2 "Soft" Functional Constraints and Pseudo-Criteria -- 2.3 More About Applying Single-Criterion Methods for Solving Multicriteria Problems -- 2.4 An Example of Optimization Problem Statement and Significant Challenge That It Presents -- 2.4.1 Expert's Difficulties -- References -- 3 Using the PSI Method and MOVI Software System for Multicriteria Analysis and Visualization -- 3.1 Performing Tests -- 3.2 Construction of Feasible and Pareto Optimal Sets -- 3.2.1 Constructing Test Tables -- 3.2.2 Constructing the Feasible Solution Set: Dialogues of an Expert with a Computer -- 3.2.3 Tables of Feasible and Pareto Optimal Solutions -- 3.2.4 Selecting the Most Preferable Solution -- 3.3 Histograms and Graphs -- 3.3.1 Design Variable Histograms: Histograms of the Distribution of Feasible Solutions.

3.3.2 Criteria Histograms: Visualization of Contradictory Criteria -- 3.3.3 Graphs "Criterion Versus Design Variable II" -- 3.3.4 Graphs "Criterion Versus Criterion" -- 3.3.5 Graphs "Criterion Versus Design Variable I" -- 3.4 Weakening Functional Constraints -- 3.4.1 Tables of Functional Failures -- References -- 4 Improving Optimal Solutions -- 4.1 Solving a New Optimization Problem -- 4.1.1 New Design Variable Constraints -- 4.1.2 Tables of Feasible and Pareto Optimal Solutions -- 4.1.3 Histograms of the Distribution of the Feasible Solutions -- 4.1.4 Graphs of Criterion Versus Design Variable II -- 4.1.5 Graphs Criterion Versus Criterion -- 4.2 Construction of the Combined Pareto Optimal Set -- 4.2.1 Basic Principles -- 4.2.2 Tables of Combined Pareto Optimal Solutions -- 4.2.3 Analysis of the Combined Pareto Optimal Set -- 4.2.4 Conclusions for Chapters 2 Through 4 -- References -- Part II Applications to Real-Life Problems -- 5 Multicriteria Design -- 5.1 Multicriteria Analysis of the Ship Design Prototype -- 5.1.1 Improvement Problem -- 5.2 Problem with the High Dimensionality of the Design Variable Vector -- 5.3 Rear Axle Housing for a Truck: PSI Method with the FiniteElement Method -- 5.3.1 General Statement of the Problem -- 5.3.2 Solution of the Problem and Analysis of the Results -- 5.3.3 Conclusions -- 5.4 Improving the Truck Frame Prototype -- 5.4.1 History of This Project -- 5.4.2 Finite Element Model of a Truck Frame -- 5.4.3 Criteria and Pseudo-Criteria -- 5.4.4 Design Variables -- 5.5 Multicriteria Optimization of Orthotropic Bridges -- 5.5.1 Introduction and Purposes -- 5.5.2 Mathematical Model and Parameters -- 5.5.3 Results of Optimization -- 5.5.4 Conclusion -- References -- 6 Multicriteria Identification -- 6.1 Adequacy of Mathematical Models -- 6.2 Multicriteria Identification and Operational Development.

6.2.1 The PSI Method in Multicriteria Identification Problems -- 6.2.2 The Search for the Identified Solutions -- 6.2.3 Operational Development of Prototypes -- 6.2.4 Conclusion -- 6.3 Vector Identification of a Spindle Unit for Metal-Cutting Machines -- 6.3.1 Introduction -- 6.3.2 Experimental Determination of the Characteristics of a Spindle Unit -- 6.3.3 Construction of Mathematical Models -- 6.3.4 The Identified Parameters of the Models -- 6.3.5 Adequacy Criteria -- 6.3.6 Solution of the Identification Problems -- 6.3.7 Solution of the Optimization Problem -- 6.3.8 Conclusion -- References -- 7 Other Multicriteria Problems and Related Issues -- 7.1 Search for the Compromise Solution When the Desired Solution Is Unattainable -- 7.1.1 Definition of the Solution That Is the Closest to the Unattainable Solution -- 7.1.2 Conclusion -- 7.2 Design of Controlled Engineering Systems -- 7.2.1 Multistage Axial Flow Compressor for an Aircraft Engine -- 7.2.2 Conclusion -- 7.3 Multicriteria Analysis from Observational Data -- 7.3.1 Example -- 7.4 Multicriteria Optimization of Large-Scale Systems in Parallel Mode -- 7.4.1 Computationally Expensive Problems -- 7.4.2 First Example -- 7.4.3 Second Example -- 7.4.4 Conclusion -- 7.5 On the Number of Trails in the Real-Life Problems -- References -- 8 Adopting the PSI Method for Database Search -- 8.1 Introduction -- 8.1.1 Characteristics of Alternatives: Criteria and Pseudo-Criteria -- 8.1.2 General Statement of the Problem and Solution Approach -- 8.1.3 Motivation of the Problem Statement -- 8.2 DBS-PSI Method -- 8.2.1 DBS-PSI Method as a New Paradigm of a Database Search -- 8.3 Searching for a Matching Partner -- 8.3.1 Conclusions of the Example -- 8.4 Summary -- References -- 9 Multicriteria Analysis of L1 Adaptive Flight Control System -- 9.1 Objective of the Research.

9.2 Prototype: Criteria and Design Variables -- 9.2.1 Design Variables -- 9.2.2 List of Criteria and Pseudo-Criteria -- 9.2.3 Criteria Addressing FQ and PIO Characteristics -- 9.2.4 Criteria Constraints -- 9.3 Solutions and Analysis -- 9.3.1 First Iteration -- 9.3.2 Second Iteration -- 9.3.3 Conclusion -- References -- Conclusions -- Appendix Examples of Calculation of the Approximate Compromise Curves -- Analytical Approach -- Example A.1 -- Example A.2 -- References -- About the AUthors -- Index.