1st Edition

Computational Intelligence-based Optimization Algorithms From Theory to Practice

By Babak Zolghadr-Asli Copyright 2024
    356 Pages 59 B/W Illustrations
    by CRC Press

    356 Pages 59 B/W Illustrations
    by CRC Press

    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.