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E-raamat: Computational Intelligence-based Optimization Algorithms: From Theory to Practice

(University of Exeter, UK, and University of Queensland, Australia)
  • Formaat: 356 pages
  • Ilmumisaeg: 11-Oct-2023
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000964721
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Computational Intelligence-based Optimization Algorithms: From Theory to PracticeSuurem pilt
  • Formaat: 356 pages
  • Ilmumisaeg: 11-Oct-2023
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000964721
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"Computational intelligence-based optimization methods, also known as meta-heuristic optimization algorithms, are a popular topic in mathematical programming. These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical, but they also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm's background and the fields it has been used in. Additionally, Python code is provided for all algorithms at the end of each chapter, making thisbook a valuable resource for beginner and intermediate programmers looking to understand these algorithms"--

In this book, we have selected some of the most effective and renowned algorithms in the literature. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in.



Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming.

These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in.

Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms.

1. An Introduction to Meta-Heuristic Optimization.
2. Pattern Search Algorithm.
3. Genetic Algorithm.
4. Simulated Annealing Algorithm.
5. Tabu Search Algorithm.
6. Ant Colony Optimization Algorithm.
7. Particle Swarm Optimization Algorithm.
8. Differential Evolution Algorithm.
9. Harmony Search Algorithm.
10. Shuffled Frog-Leaping Algorithm.
11. Invasive Weed Optimization Algorithm.
12. Biogeography-Based Optimization Algorithm.
13. Cuckoo Search Algorithm.
14. Firefly Algorithm.
15. Gravitational Search Algorithm.
16. Plant Propagation Algorithm.
17. Teaching-Learning-Based Optimization Algorithm.
18. Bat Algorithm.
19. Flower Pollination Algorithm.
20. Water Cycle Algorithm.
21. Symbiotic Organisms Search Algorithm.

Babak Zolghadr-Asli is currently a joint researcher under the QUEX program, working at the Sustainable Minerals Institute at The University of Queensland in Australia and The Centre for Water Systems at The University of Exeter in the UK. His primary research interest is to incorporate computational and artificial intelligence to understand the sustainable management of water resources.
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