Decision-Making Models : A Perspective of Fuzzy Logic and Machine Learning / Tofigh Allahviranloo, Witold Pedrycz, and Amir Seyyedabbasi, editors.

Format:

Book

Contributor:

Allahviranloo, Tofigh, editor.

Pedrycz, Witold, editor.

Seyyedabbasi, Amir, editor.

Series:

Uncertainty, Computational Techniques, and Decision Intelligence Series

Language:

English

Subjects (All):

Decision making--Mathematical models.

Decision making.

Decision making--Computer simulation.

Physical Description:

1 online resource (680 pages)

Edition:

First edition.

Place of Publication:

London, England : Academic Press, [2024]

Summary:

This book is part of the Computational Techniques, and Decision Intelligence series and focuses on advanced computational methods and decision-making processes using artificial intelligence. It covers a range of topics including neural networks, artificial intelligence algorithms, metaheuristic algorithms, optimization problems, and machine learning techniques. The book aims to provide insights into the application of these technologies in fields such as software testing, sustainable supply chain management, and data analysis. Edited by Tofigh Allahviranloo and Witold Pedrycz, it offers a comprehensive overview of both theoretical foundations and practical implementations. The intended audience includes researchers, practitioners, and students in engineering, computer science, and related disciplines. Generated by AI.

Contents:

Intro

Decision-Making Models

Copyright

Contents

Contributors

Preface

Section 1: Decision-making: New developments

Chapter 1: Neural networks

1.1. Introduction and motivation

1.2. Neural networks overview

1.3. Exploring advanced neural network concepts

1.4. Neural networks and decision-making

References

Chapter 2: Artificial intelligent algorithms, motivation, and terminology

2.1. Introduction to artificial intelligence

2.2. Types of AI algorithms

2.2.1. Supervised learning

2.2.2. Unsupervised learning

2.2.3. Semisupervised learning

2.2.4. Reinforcement learning

2.2.5. Ensemble learning

2.2.6. Deep learning

2.2.7. Neural networks

2.2.8. Convolutional neural networks (CNNs)

2.2.9. Recurrent neural networks (RNNs)

2.2.10. Generative adversarial networks (GANs)

2.2.11. Heuristic algorithms

2.2.12. Metaheuristic algorithms

2.3. Types of problems solved using artificial intelligence algorithms

2.4. Evolution of AI algorithms

Chapter 3: Decision process: A stakeholder-oriented video conferencing software selection in sustainable distance education

3.1. Introduction

3.2. Literature review

3.3. Background

3.3.1. Rational decision-making model

3.3.2. Bounded rationality decision-making model

3.3.3. Intuitive decision-making model

3.3.4. Creative decision-making model

3.4. Method

3.4.1. Preliminaries of picture fuzzy sets (PFSs)

3.4.2. MACTOR method

3.4.3. Proposed PF-MACTOR method

3.5. Analysis and findings

3.5.1. Stakeholders of SDE

3.5.2. Objectives of SDE

3.5.3. Application of PF-MACTOR method and findings

3.6. Discussion

3.7. Conclusions

Chapter 4: Learning theory

4.1. Introduction to learning theory

4.2. Ethical considerations in learning models.

4.3. Learning theory and decision-making

4.4. Future directions in learning theory

Section 2: Metaheuristic algorithms

Chapter 5: A comprehensive survey: Nature-inspired algorithms

5.1. Nature-inspired algorithms: What are they?

5.2. Motivation

5.3. Optimization

5.4. No-free-lunch

5.5. Nature-inspired metaheuristics

Chapter 6: A comprehensive survey: Physics-based algorithms

6.1. Introduction

6.2. Physics-based algorithms

6.2.1. Gravitational search algorithm

6.2.2. Atom search optimization

6.2.3. Sonar-inspired optimization

6.2.4. Vortex search algorithm

6.2.5. Lightning search algorithm

6.2.6. RIME: A physics-based optimization

6.2.7. Henry gas solubility optimization

6.2.8. Multiverse optimizer

6.3. Simulation results

Chapter 7: A comprehensive survey: Evolutionary-based algorithms

7.1. Introduction

7.2. Evolutionary strategy

7.3. Genetic algorithm

7.4. Genetic programming

7.5. Differential evolution

7.6. Discussion

Chapter 8: A comprehensive survey: Swarm-based algorithms

8.1. Introduction

8.2. Ant colony optimization

8.3. Cat swarm optimization

8.4. Elephant herding optimization

8.5. Gray wolf optimization

8.6. Harris hawks optimization

8.7. Marine predators algorithm

8.8. Particle swarm optimization

8.9. Sand cat swarm optimization

8.10. Case study: Kinematics PUMA 560

Chapter 9: Single and multi-objective metaheuristic algorithms and their applications in software maintenance

9.1. Introduction and motivation

9.2. Literature review

9.3. Heuristic-based software module clustering

9.3.1. Definition of the problem

9.3.2. Algorithm structure

9.3.3. Objective function

9.4. Experiments and results

9.4.1. Experiments platform

9.4.2. Results.

9.5. Conclusion

Chapter 10: Constraint-based heuristic algorithms for software test generation

10.1. Introduction and motivation

10.2. Literature review

10.3. Heuristic-based test data generation

10.3.1. Generating test data

10.3.2. Fitness function

10.4. Results and discussion

10.4.1. Implementation

10.4.2. Benchmarks

10.4.3. Evaluation

10.5. Conclusion

Chapter 11: Discretized optimization algorithms for finding the bug-prone locations of a program source code

11.1. Introduction and motivation

11.2. Literature review

11.3. Identifying the bug-prone paths of the programs

11.4. Experiments and results

11.5. Conclusion

Section 3: Optimization problems

Chapter 12: Mathematical programming

12.1. Introduction

12.2. Types of mathematical programming

12.3. Basic concepts

12.4. Simplex algorithm

12.5. Nonlinear programming

12.5.1. Unconstrained problems

12.6. Constrained problems with equality constraints

12.7. Lagrange multiplier method

12.8. Unconstrained problem with inequality constraints

12.9. Double search

12.10. Interval bisection method

12.11. Conclusion

Acknowledgment

Chapter 13: Discrete and combinatorial optimization

13.1. Introduction

13.1.1. Various optimization problems are divided into the following two categories

13.2. Examining search and optimization methods

13.2.1. Enumerative methods

13.2.2. Calculation methods (mathematical search or-based method calculus)

13.2.3. Innovative and meta-innovative methods (random search)

13.2.4. Combinational optimization problems

13.2.5. The method of solving combined optimization problems

13.2.5.1. Relaxation

13.2.5.2. Analysis

13.2.5.3. Column generation method

13.2.5.4. Constructive search.

13.2.5.5. Improving search

13.2.6. Neighborhood search method

13.2.7. Metaheuristic methods derived from nature

13.2.8. Traveling salesman problem (TSP)

13.3. Integer programming

13.3.1. Solution methods

13.3.1.1. Cutting plane method

13.3.1.2. Mixed algorithm

13.4. Branch-and-bound method

13.5. Additive algorithm for pure binary problem

13.5.1. Branch and bound zero-one tree

13.6. The Transportation problem

13.6.1. Fogel's approximation method

13.7. Find the optimum solution of transportation problem

13.8. Conclusion

Chapter 14: Data optimization and analysis

14.1. Introduction

14.2. Data envelopment analysis

14.3. Network data envelopment analysis

14.4. Progress and regress

14.5. Ranking

14.6. Data analysis and support vector machines

14.6.1. Separable state and data clustering

14.6.2. Nonseparable state

14.6.3. Nonlinear SVM

14.6.4. Separable SVMs in multiple categories

14.6.5. SVM applications

14.7. Conclusion

Chapter 15: Applied optimization problems

15.1. Introduction

15.2. Linear Programming

15.3. Integer programing

15.4. Nonlinear programming

15.5. Network programing

15.6. Inventory

15.7. Calculus of variations

15.8. Risk measurement

15.8.1. Standard risk measures, value-at-risk (VaR)

15.9. Mean-variance analysis

15.9.1. Diversification effect

15.10. Multiperiod binomial model

15.11. Queuing theory optimization

15.12. Supply chain concept and its applications

15.12.1. Applications of data envelopment analysis (DEA) in supply chain

15.13. Multiobjective optimization is an optimization

15.13.1. Applications of multiobjective optimization

15.13.2. Applications of optimization in reliability models.

15.13.3. Classic reliability optimization models

15.13.4. Presenting a series-parallel redundancy allocation problem (RAP)

15.13.5. Reliability optimization of a k-out-of-n series-parallel system

15.14. Conclusion

Chapter 16: Engineering optimization

16.1. Introduction

16.2. Types of optimization problems

16.2.1. ``Continuous´´ and ``discrete´´ optimization problems

16.2.2. ``Constrained´´ and ``unconstrained´´ optimization problems

16.2.3. ``Deterministic´´ and ``stochastic´´ optimization problems

16.2.4. Nonobjective, single-objective, and multiobjective optimization problems

16.2.5. Heuristic and meta-heuristic methods (random search)

16.3. Engineering optimization

16.3.1. Types of selected engineering optimization methods

16.3.2. Deterministic and probabilistic optimization algorithms

16.3.3. Direct and indirect optimization algorithms

16.3.4. Heuristic and metaheuristic optimization algorithm

16.3.4.1. Genetic algorithm

16.3.4.2. Simulated annealing

16.3.4.3. Tabu search

16.3.4.4. Particle swarm optimization algorithm

16.3.5. Comparison and selection of the appropriate optimization method

16.3.6. Advantages of the genetic algorithm compared to other optimization methods in engineering

16.4. Conclusion

Section 4: Machine learning

Chapter 17: Deep learning

17.1. Introduction and motivation

17.2. Background

17.3. Literature review

17.4. Minatar

17.5. Results and discussions

17.6. Conclusion

Chapter 18: (Artificial) neural networks

18.1. Introduction and motivation

18.2. Literature review

18.3. Artificial neural networks

18.4. Dataset

18.5. Experiments and results

18.6. Conclusion

Chapter 19: Reinforcement learning algorithms

19.1. Introduction and motivation.

19.2. Literature review.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

ISBN:

9780443161483

0443161488

OCLC:

1450092973

Publisher Number:

CIPO000104400