Particle Swarm Optimization (PSO) Algorithm
Erik Cuevas, Alma Rodríguez · 2020
A new optimization algorithm called Particle Swarm Optimization (PSO) was introduced in 1995 to become the most popular among the metaheuristic approaches. This algorithm, known as PSO , was proposed by J. Kennedy and R. Eberhart. The scheme is based on the collective behavior that some animals present when they interact in groups. Such behaviors are found in several animal groups such as a school of fish or a flock of birds. With these interactions, individuals reach a higher level of survival by collaborating together generating a kind of collective intelligence. This chapter describes the main characteristics of the PSO algorithm, as well as its search strategy considering also the solution of optimization problems. In this chapter, the operators used by the PSO are defined with the objective of analyzing their theoretical concepts involved that allow the computational implementation of the algorithm in the MATLAB ® environment. Then, the algorithm is used to solve real-world applications. The examples illustrate the use of PSO for solving problems with and without restrictions.