Modern Optimization Methods for Science, Engineering and Technology

PRELIMS.pdf
	Preface
	Acknowledgements
	Editor biography
		G R Sinha
	List of contributors
CH001.pdf
	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
CH002.pdf
	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
CH003.pdf
	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
CH004.pdf
	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
CH005.pdf
	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
CH006.pdf
	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
CH007.pdf
	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
CH008.pdf
	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
CH009.pdf
	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
CH010.pdf
	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
CH011.pdf
	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
CH012.pdf
	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
CH013.pdf
	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
CH014.pdf
	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
CH015.pdf
	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
		15.2 Cloud robotics, remote brains and their implications
			15.2.1 Cloud computing and the RoboEarth project
			15.2.2 The DAvinCi platform as a service (PaaS) surgical robot
		15.3 AI and innovations in industry
			15.3.1 Watson Analytics and data science
		15.4 Innovative solutions for a smart society using AI, robotics and the IoT
			15.4.1 Cyber-physical systems (CPSs)
			15.4.2 IoT architecture, its enabling technologies, security and privacy, and applications
			15.4.3 The Internet of robotic things (IoRT) and Industry 4.0
			15.4.4 Cloud robotics and Industry 4.0
			15.4.5 Opportunities, challenges and future directions
		15.5 The human 4.0 or the Internet of skills (IoS) and the tactile Internet (zero delay Internet)
		15.6 Future directions in robotics, AI and the IoT
		References
CH016.pdf
	Chapter 16 Efficacy of genetic algorithms for computationally intractable problems
		16.1 Introduction
		16.2 Genetic algorithm implementation
		16.3 Convergence analysis of the genetic algorithm
		16.4 Key factors
			16.4.1 Exploitation and exploration
			16.4.2 Constrained optimization
			16.4.3 Multimodal optimization
			16.4.4 Multi-objective optimization
		16.5 Concluding remarks
		References
CH017.pdf
	Chapter 17 A novel approach for QoS optimization in 4G cellular networks
		17.1 Mobile generations
		17.2 OFDMA networks
			17.2.1 Limitations of FDMA, TDMA and WCDMA networks
			17.2.2 Features of OFDMA networks
			17.2.3 Quality of service in OFDMA networks
			17.2.4 QoS improvement techniques in OFDMA networks
		17.3 Simulation model and parameters
			17.3.1 Simulation topology
			17.3.2 Performance metrics
		17.4 Adaptive rate scheduling in OFDMA networks
			17.4.1 Introduction
			17.4.2 Adaptive rate scheduling algorithm
			17.4.3 Average scheduling delay estimation for the ARS scheme
		17.5 Conclusions
		References