Modern optimization methods for science, engineering and technology /

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"Version: 20191101"--Title page verso.Includes bibliographical references.1. Introduction and background to optimization theory -- 1.1. Historical development -- 1.2. Definition and elements of optimization -- 1.3. Optimization problems and methods -- 1.4. Design and structural optimization methods -- 1.5. Optimization for signal processing and control applications -- 1.6. Design vectors, matrices, vector spaces, geometry and transforms2. Linear programming -- 2.1. Introduction -- 2.2. Applicability of LPP -- 2.3. The simplex method -- 2.4. Artificial variable techniques -- 2.5. Duality -- 2.6. Sensitivity analysis -- 2.7. Network models -- 2.8. Dual simplex method -- 2.9. Software packages to solve LPP3. 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. Conclusion4. Nonlinear optimization methods--overview and future scope -- 4.1. Introduction -- 4.2. Convex analysis -- 4.3. Applications of nonlinear optimizations techniques -- 4.4. Future research scope5. 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 -- 5.5. Improvement strategy -- 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 -- 5.11. TSPLIB and experimental results -- 5.12. Comparison experiment -- 5.13. Analysis on varying number of ants -- 5.14. IACO comparison results -- 5.15. Conclusions6. Application of a particle swarm optimization technique in a motor imagery classification problem -- 6.1. Introduction -- 6.2. Particle swarm optimization -- 6.3. Proposed method -- 6.4. Results -- 6.5. Conclusion7. Multi-criterion and topology optimization using Lie symmetries for differential equations -- 7.1. Introduction -- 7.2. Fundamentals of topological manifolds -- 7.3. Differential equations, groups and the jet space -- 7.4. Classification of the group invariant solutions and optimal solutions -- 7.5. Concluding remarks8. Learning classifier system -- 8.1. Introduction -- 8.2. Background -- 8.3. Classification learner tools -- 8.4. Sample dataset -- 8.5. Learning classifier algorithms -- 8.6. Performance -- 8.7. Conclusion9. A case study on the implementation of six sigma tools for process improvement -- 9.1. Introduction -- 9.2. Problem overview -- 9.3. Project phase summaries -- 9.4. Conclusion10. Performance evaluations and measures -- 10.1. Performance measurement models -- 10.2. AHP and fuzzy AHP -- 10.3. Performance measurement in the production approach -- 10.4. Data envelopment analysis -- 10.5. R as a tool for DEA11. Evolutionary techniques in the design of PID controllers -- 11.1. The PID controller -- 11.2. FOPID controller -- 11.3. Conclusion12. 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. Conclusion13. 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 -- 13.4. Optimization for feature selection14. 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 -- 14.2. Physical and mathematical simulation of the creation process of spatial finely dispersed structures -- 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 -- 14.4. General conclusions15. Future directions : IoT, robotics and AI based applications -- 15.1. Introduction -- 15.2. Cloud robotics, remote brains and their implications -- 15.3. AI and innovations in industry -- 15.4. Innovative solutions for a smart society using AI, robotics and the IoT -- 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 IoT16. 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 -- 16.5. Concluding remarks17. A novel approach for QoS optimization in 4G cellular networks -- 17.1. Mobile generations -- 17.2. OFDMA networks -- 17.3. Simulation model and parameters -- 17.4. Adaptive rate scheduling in OFDMA networks -- 17.5. Conclusions.Achieving a better solution or improving the performance of existing system design is an ongoing a process for which scientists, engineers, mathematicians and researchers have been striving for many years. Ever increasingly practical and robust methods have been developed, and every new generation of computers with their increased power and speed allows for the development and wider application of new types of solutions. This book defines the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. It encompasses linear programming, multivariable methods for risk assessment, nonlinear methods, ant colony optimization, particle swarm optimization, multi-criterion and topology optimization, learning classifier, case studies on six sigma, performance measures and evaluation, multi-objective optimization problems, machine learning approaches, genetic algorithms and quality of service optimizations. The book will be very useful for wide spectrum of target readers including students and researchers in academia and industry.Researchers and graduate students.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.G.R. Sinha is working as Adjunct Professor at International Institute of Information Technology, Bangalore, currently deputed as Professor at Myanmar Institute of Information Technology, Mandalay. He obtained his B.E. and M.Tech. with Gold Medal from National Institute of Technology, Raipur and his Ph.D. in Electronics & Telecommunication Engineering from Chhattisgarh Swami Vivekanand Technical University, Bhilai. He has published over 200 research papers in various international and national journals and conferences, is an active reviewer and editorial member of numerous international journals and has authored or edited six books.Title from PDF title page (viewed on December 9, 2019).

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: .,

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1 online resource (various pagings) :illustrations (some color).

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English

ISBN/ISSN

9780750324045

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519.6

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Subject(s)

Mathematical optimization.
Operations research.
Optimization.
MATHEMATICS / Optimization.

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edited by G.R. Sinha.

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