Modern optimization methods for science, engineering and technology /

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 o...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sinha, G. R. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2020]
Colección:IOP ebooks. 2020 collection.
Temas:
Mathematical optimization.
Operations research.
Optimization.
MATHEMATICS / Optimization.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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