The particle swarm optimization (PSO) was originally introduced in 1995 by Kennedy and Eberhart [1] as a tool for the optimization of nonlinear functions. The algorithm tries to simulate the behavior of animals that cooperate in groups to search for food. According to this property, the algorithm can be classified as a swarm intelligence.
However, PSO is not only based on the social interaction. Shi and Eberhart mentioned in [2] that the main decision equation of the algorithm has three basic parts; the second part performs the decision in relation to the personally best position of a particle in the design space. This part is also referred to as the cognitive part and represents the thinking of the particle. That is why the PSO can also be described as a method of artificial intelligence. The PSO tries to reach a goal (find the minimum) by using its own thinking and considering the environment that corresponds to the definition in [3].
Usage in Program
The "Optimization & Costs / CO2 Emission Estimation" add-on uses the particle swarm optimization to find the optimal assignment of the global parameters.