Linked e-resources
Details
Table of Contents
Intro
Preface
Acknowledgements
Editor biography
G R Sinha
List of contributors
Chapter 1 Introduction and background to optimization theory
1.1 Historical development
1.1.1 Robustness and optimization
1.2 Definition and elements of optimization
1.2.1 Design variables and parameters
1.2.2 Objectives
1.2.3 Constraints and bounds
1.3 Optimization problems and methods
1.3.1 Workflow of optimization methods
1.3.2 Classification of optimization methods
1.4 Design and structural optimization methods
1.4.1 Structural optimization
1.4.2 Design optimization
1.5 Optimization for signal processing and control applications
1.5.1 Signal processing optimization
1.5.2 Communication and control optimization
1.6 Design vectors, matrices, vector spaces, geometry and transforms
1.6.1 Linear algebra, matrices and design vectors
1.6.2 Vector spaces
1.6.3 Geometry, transforms, binary and fuzzy logic
References
Chapter 2 Linear programming
2.1 Introduction
2.2 Applicability of LPP
2.2.1 The product mix problem
2.2.2 Diet problem
2.2.3 Transportation problem
2.2.4 Portfolio optimization
2.3 The simplex method
2.4 Artificial variable techniques
2.5 Duality
2.6 Sensitivity analysis
2.7 Network models
2.7.1 Shortest path problem
2.8 Dual simplex method
2.9 Software packages to solve LPP
Further reading
Chapter 3 Multivariable optimization methods for risk assessment of the business processes of manufacturing enterprises
3.1 Introduction
3.2 A mathematical model of a business process
3.3 The market and specific risks, the features of their account
3.4 Measurement of the risk of using the discount rate, expert assessments and indicators of sensitivity
3.5 Conclusion
References.
Chapter 4 Nonlinear optimization methods-overview and future scope
4.1 Introduction
4.1.1 Optimization
4.1.2 NLP
4.1.3 Nonlinear optimization problem and models
4.2 Convex analysis
4.2.1 Sets and functions
4.2.2 Convex cone
4.2.3 Concave function
4.2.4 Nonlinear optimization: the interior-point approach
4.3 Applications of nonlinear optimizations techniques
4.3.1 LOQO: an interior-point code for NLP
4.3.2 Digital audio filter
4.4 Future research scope
References
Chapter 5 Implementing the traveling salesman problem using a modified ant colony optimization algorithm
5.1 ACO and candidate list
5.2 Description of candidate lists
5.3 Reasons for the tuning parameter
5.4 The improved ACO algorithm
5.4.1 Dynamic candidate set based on nearest neighbors
5.4.2 Heuristic parameter updating
5.5 Improvement strategy
5.5.1 2-Opt local search
5.6 Procedure of IACO
5.7 Flow of IACO
5.8 IACO for solving the TSP
5.9 Implementing the IACO algorithm
5.10 Experiment and performance evaluation
5.10.1 Evaluation criteria
5.10.2 Path evaluation model
5.10.3 Evaluation of solution quality
5.11 TSPLIB and experimental results
5.11.1 Experiment 1 (analysis of tour length results)
5.11.2 Experiment 2 (comparison of convergence speed)
5.12 Comparison experiment
5.13 Analysis on varying number of ants
5.13.1 Analysis of ants starting at different cities versus the same city
5.13.2 Analysis on an increasing number of ants versus number of iterations
5.14 IACO comparison results
5.15 Conclusions
References
Chapter 6 Application of a particle swarm optimization technique in a motor imagery classification problem
6.1 Introduction
6.1.1 Literature review
6.1.2 Motivation and requirements
6.2 Particle swarm optimization.
6.2.1 The mathematical model of PSO
6.2.2 Constraint-based optimization
6.3 Proposed method
6.3.1 Materials and methods
6.3.2 Classification
6.4 Results
6.5 Conclusion
References
Chapter 7 Multi-criterion and topology optimization using Lie symmetries for differential equations
7.1 Introduction
7.2 Fundamentals of topological manifolds
7.2.1 Analytic manifolds
7.2.2 Lie groups and vector fields
7.3. Differential equations, groups and the jet space
7.3.1 Prolongation of group action and vector fields
7.3.2 Total derivatives of vector fields and general prolongation formula
7.3.3 Criterion of maximal rank and infinitesimal invariance for differential equations
7.3.4 Differential equations and symmetry groups
7.3.5 Differential invariants and the group invariant solutions
7.4 Classification of the group invariant solutions and optimal solutions
7.4.1 Adjoint representation for the cKdV and optimization of the group generators
7.4.2 Calculation of the optimal group invariant solutions for the cKdV
7.5 Concluding remarks
References
Chapter 8 Learning classifier system
8.1 Introduction
8.2 Background
8.3 Classification learner tools
8.3.1 MATLAB®: classification learner app
8.3.2 BigML®
8.3.3 Microsoft® AzureML®
8.4 Sample dataset
8.4.1 Splitting the dataset
8.5 Learning classifier algorithms
8.5.1 Logistic regression classifiers
8.5.2 Decision tree classifiers
8.5.3 Discriminant analysis classifiers
8.5.4 Support vector machine classifiers
8.5.5 Nearest neighbor classifiers
8.5.6 Ensemble classifiers
8.6 Performance
8.6.1 Confusion matrix
8.6.2 Receiver operating characteristic
8.6.3 Parallel plot
8.7 Conclusion
Acknowledgments
References.
Chapter 9 A case study on the implementation of six sigma tools for process improvement
9.1 Introduction
9.1.1 Generation and cleaning of BF gas
9.2 Problem overview
9.3 Project phase summaries
9.3.1 Definition
9.3.2 Measurement
9.3.3 Analyze and improvement
9.3.4 Control
9.4 Conclusion
9.4.1 Financial benefits
9.4.2 Non-financial benefits
Chapter 10 Performance evaluation and measures
10.1 Performance measurement models
10.1.1 Fuzzy sets
10.2 AHP and fuzzy AHP
10.2.1 Fuzzy AHP
10.2.2 Linear programming method
10.3 Performance measurement in the production approach
10.3.1 Free disposability hull
10.4 Data envelopment analysis
10.4.1 CCR model
10.4.2 BCC model
10.4.3 Other models
10.5 R as a tool for DEA
References
Chapter 11 Evolutionary techniques in the design of PID controllers
11.1 PID controller
11.1.1 Design procedure
11.1.2 Method 1: PID controller design using PSO
11.1.3 Method 2: PID controller design using BBBC
11.2 FOPID controller
11.2.1 Statement of the problem
11.2.2 BBBC aided tuning of FOPID controller parameters
11.2.3 Illustrative examples
11.3 Conclusion
References
Chapter 12 A variational approach to substantial efficiency for linear multi-objective optimization problems with implications for market problems
12.1 Introduction
12.2 Background
12.3 A review of substantial efficiency
12.4 New results and examples
12.5 Conclusion
References
Chapter 13 A machine learning approach for engineering optimization tasks
13.1 Optimization: classification hierarchy
13.2 Optimization problems in machine learning
13.3 Optimization in supervised learning
13.3.1 Bayesian optimization
13.3.2 Bayesian optimization for weight computation: a case study
13.3.3 Bayesian optimal classification: a case study.
13.3.4 Bayesian optimization via binary classification: a case study
13.4 Optimization for feature selection
13.4.1 Feature extraction using precedence relations: a case study
13.4.2 Feature extraction via ensemble pruning: a case study
13.4.3 Feature-vector ranking metrics
References
Chapter 14 Simulation of the formation process of spatial fine structures in environmental safety management systems and optimization of the parameters of dispersive devices
14.1 The use of spatial finely dispersed multiphase structures in ensuring ecological and technogenic safety
14.1.1 Analysis of recent research and publications
14.1.2 Statement of the problem and its solution
14.2 Physical and mathematical simulation of the creation process of spatial finely dispersed structures
14.2.1 Gas phase study and mathematical model description
14.2.2 Dispersed phase study and mathematical model description
14.2.3 Mathematical model of interfacial interaction
14.3 Numerical simulation of the formation of spatial dispersed structures and the determination of the most effective ways of supplying fluid to eliminate various hazards
14.3.1 Ensuring numerical solution stability, convergence and accuracy
14.3.2 Description of the numerical integration method of the dispersed phase equations
14.3.3 Results of numerical simulation of a spatial finely dispersed structure creation process which suppresses dust
14.3.4 Results of numerical simulation of the spatial finely dispersed structure creation process, which instantly reduces the gas stream temperature
14.4 General conclusions
References
Chapter 15 Future directions: IoT, robotics and AI based applications
15.1 Introduction
15.1.1 The impact of AI and robotics in medicine and healthcare
15.1.2 Advances in AI technology and their impact on the workforce.
15.1.3 AI technologies and human intelligence.
Preface
Acknowledgements
Editor biography
G R Sinha
List of contributors
Chapter 1 Introduction and background to optimization theory
1.1 Historical development
1.1.1 Robustness and optimization
1.2 Definition and elements of optimization
1.2.1 Design variables and parameters
1.2.2 Objectives
1.2.3 Constraints and bounds
1.3 Optimization problems and methods
1.3.1 Workflow of optimization methods
1.3.2 Classification of optimization methods
1.4 Design and structural optimization methods
1.4.1 Structural optimization
1.4.2 Design optimization
1.5 Optimization for signal processing and control applications
1.5.1 Signal processing optimization
1.5.2 Communication and control optimization
1.6 Design vectors, matrices, vector spaces, geometry and transforms
1.6.1 Linear algebra, matrices and design vectors
1.6.2 Vector spaces
1.6.3 Geometry, transforms, binary and fuzzy logic
References
Chapter 2 Linear programming
2.1 Introduction
2.2 Applicability of LPP
2.2.1 The product mix problem
2.2.2 Diet problem
2.2.3 Transportation problem
2.2.4 Portfolio optimization
2.3 The simplex method
2.4 Artificial variable techniques
2.5 Duality
2.6 Sensitivity analysis
2.7 Network models
2.7.1 Shortest path problem
2.8 Dual simplex method
2.9 Software packages to solve LPP
Further reading
Chapter 3 Multivariable optimization methods for risk assessment of the business processes of manufacturing enterprises
3.1 Introduction
3.2 A mathematical model of a business process
3.3 The market and specific risks, the features of their account
3.4 Measurement of the risk of using the discount rate, expert assessments and indicators of sensitivity
3.5 Conclusion
References.
Chapter 4 Nonlinear optimization methods-overview and future scope
4.1 Introduction
4.1.1 Optimization
4.1.2 NLP
4.1.3 Nonlinear optimization problem and models
4.2 Convex analysis
4.2.1 Sets and functions
4.2.2 Convex cone
4.2.3 Concave function
4.2.4 Nonlinear optimization: the interior-point approach
4.3 Applications of nonlinear optimizations techniques
4.3.1 LOQO: an interior-point code for NLP
4.3.2 Digital audio filter
4.4 Future research scope
References
Chapter 5 Implementing the traveling salesman problem using a modified ant colony optimization algorithm
5.1 ACO and candidate list
5.2 Description of candidate lists
5.3 Reasons for the tuning parameter
5.4 The improved ACO algorithm
5.4.1 Dynamic candidate set based on nearest neighbors
5.4.2 Heuristic parameter updating
5.5 Improvement strategy
5.5.1 2-Opt local search
5.6 Procedure of IACO
5.7 Flow of IACO
5.8 IACO for solving the TSP
5.9 Implementing the IACO algorithm
5.10 Experiment and performance evaluation
5.10.1 Evaluation criteria
5.10.2 Path evaluation model
5.10.3 Evaluation of solution quality
5.11 TSPLIB and experimental results
5.11.1 Experiment 1 (analysis of tour length results)
5.11.2 Experiment 2 (comparison of convergence speed)
5.12 Comparison experiment
5.13 Analysis on varying number of ants
5.13.1 Analysis of ants starting at different cities versus the same city
5.13.2 Analysis on an increasing number of ants versus number of iterations
5.14 IACO comparison results
5.15 Conclusions
References
Chapter 6 Application of a particle swarm optimization technique in a motor imagery classification problem
6.1 Introduction
6.1.1 Literature review
6.1.2 Motivation and requirements
6.2 Particle swarm optimization.
6.2.1 The mathematical model of PSO
6.2.2 Constraint-based optimization
6.3 Proposed method
6.3.1 Materials and methods
6.3.2 Classification
6.4 Results
6.5 Conclusion
References
Chapter 7 Multi-criterion and topology optimization using Lie symmetries for differential equations
7.1 Introduction
7.2 Fundamentals of topological manifolds
7.2.1 Analytic manifolds
7.2.2 Lie groups and vector fields
7.3. Differential equations, groups and the jet space
7.3.1 Prolongation of group action and vector fields
7.3.2 Total derivatives of vector fields and general prolongation formula
7.3.3 Criterion of maximal rank and infinitesimal invariance for differential equations
7.3.4 Differential equations and symmetry groups
7.3.5 Differential invariants and the group invariant solutions
7.4 Classification of the group invariant solutions and optimal solutions
7.4.1 Adjoint representation for the cKdV and optimization of the group generators
7.4.2 Calculation of the optimal group invariant solutions for the cKdV
7.5 Concluding remarks
References
Chapter 8 Learning classifier system
8.1 Introduction
8.2 Background
8.3 Classification learner tools
8.3.1 MATLAB®: classification learner app
8.3.2 BigML®
8.3.3 Microsoft® AzureML®
8.4 Sample dataset
8.4.1 Splitting the dataset
8.5 Learning classifier algorithms
8.5.1 Logistic regression classifiers
8.5.2 Decision tree classifiers
8.5.3 Discriminant analysis classifiers
8.5.4 Support vector machine classifiers
8.5.5 Nearest neighbor classifiers
8.5.6 Ensemble classifiers
8.6 Performance
8.6.1 Confusion matrix
8.6.2 Receiver operating characteristic
8.6.3 Parallel plot
8.7 Conclusion
Acknowledgments
References.
Chapter 9 A case study on the implementation of six sigma tools for process improvement
9.1 Introduction
9.1.1 Generation and cleaning of BF gas
9.2 Problem overview
9.3 Project phase summaries
9.3.1 Definition
9.3.2 Measurement
9.3.3 Analyze and improvement
9.3.4 Control
9.4 Conclusion
9.4.1 Financial benefits
9.4.2 Non-financial benefits
Chapter 10 Performance evaluation and measures
10.1 Performance measurement models
10.1.1 Fuzzy sets
10.2 AHP and fuzzy AHP
10.2.1 Fuzzy AHP
10.2.2 Linear programming method
10.3 Performance measurement in the production approach
10.3.1 Free disposability hull
10.4 Data envelopment analysis
10.4.1 CCR model
10.4.2 BCC model
10.4.3 Other models
10.5 R as a tool for DEA
References
Chapter 11 Evolutionary techniques in the design of PID controllers
11.1 PID controller
11.1.1 Design procedure
11.1.2 Method 1: PID controller design using PSO
11.1.3 Method 2: PID controller design using BBBC
11.2 FOPID controller
11.2.1 Statement of the problem
11.2.2 BBBC aided tuning of FOPID controller parameters
11.2.3 Illustrative examples
11.3 Conclusion
References
Chapter 12 A variational approach to substantial efficiency for linear multi-objective optimization problems with implications for market problems
12.1 Introduction
12.2 Background
12.3 A review of substantial efficiency
12.4 New results and examples
12.5 Conclusion
References
Chapter 13 A machine learning approach for engineering optimization tasks
13.1 Optimization: classification hierarchy
13.2 Optimization problems in machine learning
13.3 Optimization in supervised learning
13.3.1 Bayesian optimization
13.3.2 Bayesian optimization for weight computation: a case study
13.3.3 Bayesian optimal classification: a case study.
13.3.4 Bayesian optimization via binary classification: a case study
13.4 Optimization for feature selection
13.4.1 Feature extraction using precedence relations: a case study
13.4.2 Feature extraction via ensemble pruning: a case study
13.4.3 Feature-vector ranking metrics
References
Chapter 14 Simulation of the formation process of spatial fine structures in environmental safety management systems and optimization of the parameters of dispersive devices
14.1 The use of spatial finely dispersed multiphase structures in ensuring ecological and technogenic safety
14.1.1 Analysis of recent research and publications
14.1.2 Statement of the problem and its solution
14.2 Physical and mathematical simulation of the creation process of spatial finely dispersed structures
14.2.1 Gas phase study and mathematical model description
14.2.2 Dispersed phase study and mathematical model description
14.2.3 Mathematical model of interfacial interaction
14.3 Numerical simulation of the formation of spatial dispersed structures and the determination of the most effective ways of supplying fluid to eliminate various hazards
14.3.1 Ensuring numerical solution stability, convergence and accuracy
14.3.2 Description of the numerical integration method of the dispersed phase equations
14.3.3 Results of numerical simulation of a spatial finely dispersed structure creation process which suppresses dust
14.3.4 Results of numerical simulation of the spatial finely dispersed structure creation process, which instantly reduces the gas stream temperature
14.4 General conclusions
References
Chapter 15 Future directions: IoT, robotics and AI based applications
15.1 Introduction
15.1.1 The impact of AI and robotics in medicine and healthcare
15.1.2 Advances in AI technology and their impact on the workforce.
15.1.3 AI technologies and human intelligence.