Author
Keywords
Particle Swarm, populationbased methods
Review Status
Unreviewed
The Algorithm
Particle Swarm Optimisation (PS) is a populationbased method and was proposed for the optimisation of continuous objective functions [1]. While the metaphor for other populationbased methods (like Differential Evolution or Genetic Algorithms) was evolution, the narrative for PS is based on flocks of birds that search for food.
The initial population consists of $n_P$ solutions that are represented by realvalued vectors. In each iteration, each solution is updated by adding another vector called velocity $v_i$. This velocity changes over the course of the optimisation. More specifically, at the start of each iteration the directions towards the best solution found so far by the particular solution, $Pbest_i$, and the best overall solution, $Pbest_{gbest}$, are determined. The sum of these two directions (which are the differences between the respective vectors, see Line 7 in the pseudocode below) are perturbed by multiplication with a uniform random variable $u$ and a constant $c$. The vector so obtained is added to the previous $v_i$; the resulting updated velocity is added to the respective solution. In some implementations, the velocities are reduced in every generation by setting the inertia parameter $\delta$ to a value smaller than unity.
Internal Links
Concepts 
Tutorials 
Tips 
Related Articles 
External links
References 
1. Eberhart, R. C. and J. Kennedy (1995). A new optimizer using particle swarm theory, in: Proceedings of the Sixth International Symposium on Micromachine and Human Science. Nagoya, Japan.

Weblinks 