Handbook of metaheuristic algorithms : from fundamental theories to advanced applications / Chun-Wei Tsai and Ming-Chao Chiang.

Format:

Book

Author/Creator:

Tsai, Chun-Wei, author.

Chiang, Ming-Chao, author.

Series:

Uncertainty, computational techniques, and decision intelligence.

Uncertainty, computational techniques, and decision intelligence

Language:

English

Subjects (All):

Metaheuristics.

Physical Description:

1 online resource (xxxviii, 584 pages) : illustrations (black & white).

Place of Publication:

London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2023]

Summary:

Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.

Contents:

Front Cover

Handbook of Metaheuristic Algorithms

Copyright

Contents

List of figures

List of tables

List of algorithms

List of listings

About the authors

Chun-Wei Tsai (1978-)

Ming-Chao Chiang (1956-)

Preface

Part 1 Fundamentals

1 Introduction

1.1 Why metaheuristic algorithms

1.2 Organization of this book

2 Optimization problems

2.1 Problem definition

2.2 Combinatorial optimization problems

2.2.1 The one-max and 0-1 knapsack problems

2.2.2 The B2D and deceptive problems

2.2.3 The traveling salesman problem (TSP)

2.3 Continuous optimization problems

2.3.1 The single-objective optimization problem

2.3.2 The multi-objective optimization problem

2.4 Summary

3 Traditional methods

3.1 Exhaustive search (ES)

3.1.1 The basic idea of ES

3.1.2 Implementation of ES for the one-max problem

3.1.3 Discussion of ES

3.2 Hill climbing (HC)

3.2.1 The basic idea of HC

3.2.2 Implementation of HC for the one-max problem

3.2.2.1 Main function

3.2.2.2 Search function of HC

3.2.2.2.1 Declaration of parameters and functions

3.2.2.2.2 The main loop

3.2.2.2.3 Additional functions

3.2.2.3 Library function

3.2.3 Discussion of HC

3.3 Comparisons between ES and HC

3.3.1 Simulation results of ES and HC for the one-max problem

3.3.2 Simulation results of ES and HC for the deceptive problem

3.4 Summary of ES and HC

Supplementary source code

4 Metaheuristic algorithms

4.1 What is a metaheuristic algorithm?

4.2 A unified framework for metaheuristic algorithms

4.3 Comparisons of metaheuristics with exhaustive and greedy search

5 Simulated annealing

5.1 The basic idea of simulated annealing (SA)

5.2 Implementation of SA for the one-max and deceptive problems

5.2.1 Declaration of functions and parameters

5.2.2 The main loop.

5.2.3 Additional functions

5.3 Simulation results of SA

5.3.1 Simulation results of SA for the one-max problem

5.3.2 Simulation results of SA for the deceptive problem

5.4 Discussion

6 Tabu search

6.1 The basic idea of tabu search (TS)

6.2 Implementation of TS for the one-max and deceptive problems

6.2.1 Declaration of functions and parameters

6.2.2 The main loop

6.2.3 Additional functions

6.3 Simulation results of TS

6.3.1 Simulation results of TS for the one-max problem

6.3.2 Simulation results of TS for the deceptive problem

6.4 Discussion

7 Genetic algorithm

7.1 The basic idea of genetic algorithm (GA)

7.2 Implementation of GA for the one-max and deceptive problems

7.2.1 Declaration of functions and parameters

7.2.2 The main loop

7.2.3 Additional functions

7.3 Simulation results of GA

7.3.1 Simulation results of GA for the one-max problem

7.3.2 Simulation results of GA for the deceptive problem

7.4 Discussion

8 Ant colony optimization

8.1 The basic idea of ant colony optimization (ACO)

8.1.1 The ant system (AS)

8.1.2 The ant colony system (ACS)

8.2 Implementation of ACO for the traveling salesman problem

8.2.1 Declaration of functions and parameters

8.2.2 The main loop

8.2.3 Additional functions

8.3 Simulation results of ACO for the traveling salesman problem

8.4 Discussion

9 Particle swarm optimization

9.1 The basic idea of particle swarm optimization (PSO)

9.2 Implementation of PSO for the function optimization problem

9.2.1 Declaration of functions and parameters

9.2.2 The main loop

9.2.3 Additional functions

9.3 Simulation results of PSO for the function optimization problem

9.4 Discussion.

Supplementary source code

10 Differential evolution

10.1 The basic idea of differential evolution (DE)

10.2 Implementation of DE for the function optimization problem

10.2.1 Declaration of functions and parameters

10.2.2 The main loop

10.2.3 Additional functions

10.2.4 Other mutation strategies

10.3 Simulation results of DE for the function optimization problem

10.4 Discussion

Part 2 Advanced technologies

11 Solution encoding and initialization operator

11.1 Encoding of solutions

11.2 Initialization operator

11.3 Discussion

12 Transition operator

12.1 Why use different transition operators

12.2 Different transition operators of GA for solving the TSP

12.3 Implementation of GA for the TSP with different crossover operators

12.3.1 Declaration of functions and parameters

12.3.2 The main loop

12.3.3 Additional functions

12.3.4 Adding new crossover operators

12.4 Simulation results of GA for the TSP with different crossover operators

12.5 Discussion

13 Evaluation and determination operators

13.1 Evaluation operator

13.2 Determination operator

13.2.1 Determination operator for single-solution-based metaheuristic algorithms

13.2.2 Determination operator for population-based metaheuristic algorithms

13.3 Schema theorem

13.3.1 Selection and fitness function for the schema

13.3.2 Crossover for the schema

13.3.3 Mutation for the schema

13.3.4 A simple example of schema theory

13.4 Fitness landscape analysis

13.5 Discussion

14 Parallel metaheuristic algorithm

14.1 The basic idea of the parallel metaheuristic algorithm

14.1.1 Single-solution-based parallel metaheuristic algorithms

14.1.2 Population-based parallel metaheuristic algorithms.

14.2 Implementation of parallel GA for the TSP

14.2.1 Declaration of functions and parameters

14.2.2 The main loop

14.2.3 Additional functions

14.3 Simulation results of parallel GA for the TSP

14.4 Discussion

15 Hybrid metaheuristic and hyperheuristic algorithms

15.1 The basic idea of the hybrid metaheuristic algorithm

15.2 The basic idea of the hyperheuristic algorithm

15.3 Implementation of the hybrid heuristic algorithm for the TSP

15.3.1 Declaration of functions and parameters

15.3.2 The main loop of HGA

15.3.3 Additional functions of HGA

15.3.4 The declaration of functions and parameters of SA

15.3.5 The main loop of SA

15.3.6 Additional functions of SA

15.4 Simulation results of the hybrid heuristic algorithm for the TSP

15.5 Discussion

16 Local search algorithm

16.1 The basic idea of local search

16.1.1 Iterating with different solutions

16.1.2 Changing the landscape of the problem

16.1.3 Accepting non-improving neighbors

16.1.4 k-opt

16.2 Metaheuristic algorithm with local search

16.3 Implementation of GA with 2-opt for the TSP

16.3.1 Declaration of functions and parameters

16.3.2 The main loop

16.3.3 Additional functions

16.4 Simulation results of GA with 2-opt for the TSP

16.5 Discussion

17 Pattern reduction

17.1 The basic idea of pattern reduction

17.2 Implementation of PREGA for clustering problems

17.2.1 Declaration of functions and parameters

17.2.2 The main loop

17.2.3 Additional functions

17.3 Simulation results of PREGA for clustering problems

17.4 Related work

17.5 Discussion

18 Search economics

18.1 The basic idea of search economics

18.1.1 The resource arrangement operator.

18.1.2 The vision search operator

18.1.3 The marketing research operator

18.2 Implementation of SE for the one-max problem

18.2.1 Declaration of functions and parameters

18.2.2 The main loop

18.2.3 Additional functions

18.3 Simulation results of SE for the one-max problem

18.4 Discussion

19 Advanced applications

19.1 Data clustering

19.1.1 Problem description and definition

19.1.2 Solution encoding

19.1.3 Metaheuristic algorithm for data clustering

19.2 Cluster-head selection

19.2.1 Problem description and definition

19.2.2 Solution encoding

19.2.3 Metaheuristic algorithm for cluster-head selection

19.3 Traffic light control

19.3.1 Problem description and definition

19.3.2 Solution encoding

19.3.3 Metaheuristic algorithm for traffic light control

19.4 Hyperparameter optimization

19.4.1 Problem description and definition

19.4.2 Solution encoding

19.4.3 Metaheuristic algorithm for hyperparameter optimization

19.5 Convolutional neural network filter pruning

19.5.1 Problem description and definition

19.5.2 Solution encoding

19.5.3 Metaheuristic algorithm for convolutional neural network pruning

19.6 Discussion

20 Conclusion and future research directions

20.1 Conclusion

20.2 Future research directions

A Interpretations and analyses of simulation results

A.1 Interpretations of metaheuristics

A.1.1 Quality of the end result

A.1.2 Convergence curves

A.1.3 Number of evaluations and computation time

A.2 Analyses of metaheuristics

A.2.1 Impact of parameters and operators

A.2.2 Complexity and statistical analyses

A.3 Discussion

B Implementation in Python

References

Index

Back Cover.

Notes:

Includes bibliographical references (pages 553-574) and index.

Description based on print version record.

<b>PART 1 Fundamentals</b> <p>1. Introduction</p> <p>2. Optimization problems</p> <p>3. Traditional methods</p> <p>4. Metaheuristic algorithms</p> <p>5. Simulated annealing</p> <p>6. Tabu search</p> <p>7. Genetic algorithm</p> <p>8. Ant colony optimization</p> <p>9. Particle swarm optimization</p> <p>10. Differential evolution</p> <p><b>PART 2 Advanced technologies</b></p> <p>11. Solution encoding and initialization operator</p> <p>12. Transition operator</p> <p>13. Evaluation and determination operators</p> <p>14. Parallel metaheuristic algorithm</p> <p>15. Hybrid metaheuristic and hyperheuristic algorithms</p> <p>16. Local search algorithm</p> <p>17. Pattern reduction</p> <p>18. Search economics</p> <p>19. Advanced applications</p> <p>20. Conclusion and future research directions</p> <p>A. Interpretations and analyses of simulation results</p> <p>B. Implementation in Python</p>

Other Format:

Print version: Tsai, Chun-Wei Handbook of Metaheuristic Algorithms

ISBN:

0-443-19109-3

9780443191091

OCLC:

1380993852