Textbook in PDF format

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.
We have handpicked some of the literature’s most exciting and well-​known algorithms. Not only are these algorithms very efficient from a practical point of view, but they all also consist of through-​provoking theoretical ideas that can help the reader better understand how these algorithms actually tend to solve an optimization problem. In this book, we will learn about pattern search, genetic algorithm, simulated annealing, tabu search, ant colony optimization, particle swarm optimization, differential evolution algorithm, harmony search algorithm, shuffle frog-​leaping algorithm, invasive weed optimization, biogeography-​based optimization, cuckoo search algorithm, firefly algorithm, gravity search algorithm, plant propagation algorithm, teaching-​learning-​based algorithm, bat algorithm, flower pollination algorithm, water cycle algorithm, and symbiotic organisms algorithm. Though the chapters are arranged chronically, there are some pedagogical reasoning behind this arrangement so that the readers can easily engage with the presented materials in each chapter. Note that the basic idea here is to ensure that each algorithm is presented in a stand-​alone chapter.
Each chapter also contains a brief literature review of the algorithm’s background and showcases where it has been implemented successfully. As stated earlier, there is a Python code for all algorithms at the end of each chapter. It is important to note that, while these are not the most efficient way to code these algorithms, they may very well be the best way to understand them for beginner to intermediate programmers. As such, if, as a reader, you have a semi-​solid understanding of the Python syntax and its numeric library NumPy, you could easily understand and implement these methods on your own