Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications

Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for 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.

Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up
from the basic ideas to advanced solutions. Readers will be able to understand metaheuristic algorithms and how to use them to solve problems across a wide range of scientific and engineering fields. 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, expanding the gap between theory and implementation. This book can also help students and researchers to construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.