Particle Swarm Optimization

Particle Swarm Optimization is an algorithm capable of optimizing a non-linear and multidimensional problem which usually reaches good solutions efficiently while requiring minimal parameterization. The algorithm and its concept of “Particle Swarm Optimization”(PSO) were introduced by James Kennedy and Russel Ebhart in 1995 [4]. However, its origins go further backwards since the basic principle of optimization by swarm is inspired in previous attempts at reproducing observed behaviors of animals in their natural habitat, such as bird flocking or fish schooling, and thus ultimately its origins are nature itself. These roots in natural processes of swarms lead to the categorization of the algorithm as one of Swarm Intelligence and Artificial Life. The basic concept of the algorithm is to create a swarm of particles which move in the space around them (the problem space) searching for their goal or the place which best suits their needs given by a fitness function. A nature analogy with birds is the following: a bird flock flies in its environment looking for the best place to rest (the best place can be a combination of characteristics like space for all the flock, food access, water access or any other relevant characteristic). Based on this simple concept there are two main ideas behind its optimization properties: