Documentation/Modules/OptAlgMOPSO
Inhaltsverzeichnis
Summary
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).
Properties
General
| 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. |
Connections
| Starting at this module | Module requires exactly one connection of type optimization.
|
|---|---|
| Ending at this module | - |
Actions
| Name | Description |
|---|---|
| Run | starts the optimization. |
Options
The options are currently described in the pop-up help.
Module Description
Initialization
The initial particles are randomly generated within the initial search region (if existing) or otherwise between the bounds.
Optimization
The algorithm contains stochastic processes and operates with a set of particles. Parallelization on the basis of the number of particles is implemented.
Usage
... todo
Source Code
References
(1) Sanaz Mostaghim. Multi-Objective Evolutionary Algorithms. Data Structures, Convergence, and Diversity. Paderborn, Germany, November 2004.
