The particle swarm optimizationPSO belongs to the class of direct search methods used to find an optimal solution to an objective function (aka [[fitness function]]) in a search space. Direct search methods are usually derivative-free, meaning that they depend only on the evaluation of the objective function. The particle swarm optimization algorithm is simple, in the sense that the even the basic form of the algorithm yields results, it can be implemented by a programmer in short duration, and it can be used by anyone with an understanding of objective functions and the problem at hand without needing an extensive background in mathematical [[Optimization_(mathematics)|optimization theory]].
The particle swarm optimizationPSO is a [[stochastic]], population-based computer algorithm modelled on [[swarm intelligence]]. Swarm intelligence is based on [[social psychology|social-psychological]] principles and provides insights into [[social behavior]], as well as contributing to engineering applications. The particle swarm optimization algorithm was first described in 1995 by [[James Kennedy (social psychologist)|James Kennedy]] and [[Russell C. Eberhart]].
[[Social influence]] and [[social learning]] enable a person to maintain [[cognitive consistency]]. People solve problems by talking with other people about them, and as they interact their beliefs, attitudes, and behaviors change; the changes could typically be depicted as the individuals moving toward one another in a socio-cognitive space.
