This optimization module is an implementation of the particle swarm optimization algorithm for single- and multi-objective optimization (1), however it contains some modifications to the publication. The algorithm reflects the natural movement of flocking birds.
The algorithm is elitist: Always the best particles are kept as guides.
This algorithm is designed for continuous variables and can not handle discrete problems. Furthermore, the algorithm is implemented for minimizing a single and multiple objective function(s).
|Algorithm||stochastic - stochastic adaptation of the velocities.|
|Design Variables||Written for continuous variables. No discrete or mixed variables are possible.|
|Objectives||single- and multi-objective for minimization.|
|Initialization||Requires at least one of the following: initial search region or bounds.|
|Starting at this module|| Module requires exactly one connection of type |
|Ending at this module||-|
|Run||starts the optimization.|
The options are currently described in the pop-up help.
The initial particles are randomly generated within the
initial search region (if existing) or otherwise between the
The algorithm contains stochastic processes and operates with a set of particles. Parallelization on the basis of the number of particles is implemented.
(1) Sanaz Mostaghim. Multi-Objective Evolutionary Algorithms. Data Structures, Convergence, and Diversity. Paderborn, Germany, November 2004.