Modern optimization methods for science, engineering and technology / edited by G.R. Sinha.

Format:

Book

Contributor:

Sinha, G. R., 1975- editor.

Institute of Physics (Great Britain), publisher.

Series:

IOP ebooks. 2020 collection.

IOP ebooks. [2020 collection]

Language:

English

Subjects (All):

Mathematical optimization.

Operations research.

Physical Description:

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

Place of Publication:

Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2020]

System Details:

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

text file

Biography/History:

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 and 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.

Summary:

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.

Contents:

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

13.4. Optimization for feature selection

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

14.2. 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 conclusions

15. 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 IoT

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

16.5. Concluding remarks

17. 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.

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 transforms

2. 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 LPP

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

4. 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 scope

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

5.5. 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. Conclusions

6. 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. Conclusion

7. 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 remarks

8. 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. Conclusion

9. 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. Conclusion

10. 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 DEA

11. Evolutionary techniques in the design of PID controllers

11.1. The PID controller

11.2. FOPID controller

11.3. Conclusion

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

Notes:

"Version: 20191101"--Title page verso.

Includes bibliographical references.

Title from PDF title page (viewed on December 9, 2019).

Other Format:

Print version:

ISBN:

9780750324045

9780750324038

OCLC:

1130295074

Access Restriction:

Restricted for use by site license.