Abstract
Bioinspired algorithms have been employed in situations where conventional optimization techniques cannot find a satisfactory solution, for example, when the function to be optimized is discontinuous, nondifferentiable, and/or presents too many nonlinearly related parameters (Floreano and Mattiussi, Bio-inspired artificial intelligence: Theories, methods, and technologies, 2008). One of the most well-known bioinspired algorithms used in optimization problems is particle swarm optimization (PSO), which basically consists of a machine-learning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. This beginning chapter aims to introduce the main mechanics behind the traditional PSO, outlining its advantages and disadvantages, as well as summarizing the several extensions proposed in the literature over the past almost 20 years.
