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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.
Constraint handling no
Boundary handling no
Initialization Requires at least one of the following: initial search region or bounds.


Starting at this module Module requires exactly one connection of type optimization.
Ending at this module -


Name Description
Run starts the optimization.


The options are currently described in the pop-up help.

Module Description


The initial particles are randomly generated within the initial search region (if existing) or otherwise between the bounds.


The algorithm contains stochastic processes and operates with a set of particles. Parallelization on the basis of the number of particles is implemented.


... todo

Source Code


(1) Sanaz Mostaghim. Multi-Objective Evolutionary Algorithms. Data Structures, Convergence, and Diversity. Paderborn, Germany, November 2004.