Modern Optimization Methods for Science, Engineering and Technology.
Sinha, G. R.
Modern Optimization Methods for Science, Engineering and Technology. - 1st ed. - 1 online resource (433 pages) - IOP Ebooks Series . - IOP Ebooks Series .
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.
This book reviews the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. The book will be useful for a wide spectrum of target readers such as research scholars, academics and industry professionals.
9780750324045
Mathematical optimization.
Operations research.
Electronic books.
QA402.5 .M634 2020
519.3
Modern Optimization Methods for Science, Engineering and Technology. - 1st ed. - 1 online resource (433 pages) - IOP Ebooks Series . - IOP Ebooks Series .
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.
This book reviews the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. The book will be useful for a wide spectrum of target readers such as research scholars, academics and industry professionals.
9780750324045
Mathematical optimization.
Operations research.
Electronic books.
QA402.5 .M634 2020
519.3