1st Edition
Computational Intelligence-based Optimization Algorithms From Theory to Practice
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
Biography
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.
