Documentation/Modules/OptAlgMOPSO

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

ToDo:Link to SVN

References

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