Multi-Objective Particle Swarm Optimization (MOPSO)

Bearable and compressed implementation of Multi-Objective Particle Swarm Optimization (MOPSO)
8.3K descargas
Actualizado 27 Nov 2019

Ver licencia

This function performs a Multi-Objective Particle Swarm Optimization (MOPSO) for minimizing continuous functions. The implementation is bearable, computationally cheap, and compressed (the algorithm only requires one file: MPSO.m). An 'example.m' script is provided in order to help users to use the implementation. It is also noteworthy to mention that the code is highly commented for easing the understanding. This implementation is based on the paper of Coello et al. (2004), "Handling multiple objectives with particle swarm optimization".

IMPORTANT: the objetive function that you specify must be vectorized. This means that it will take the entire population (i.e., a matrix Np x nVar, which Np is the number of particles and nVar is the number of variables) and it expects to receive a fitness value for each particle (i.e., a vector Np x 1). If the function is not vectoriyed and receives only a single value, the code will obviously rise an error.

Citar como

Víctor Martínez-Cagigal (2024). Multi-Objective Particle Swarm Optimization (MOPSO) (https://www.mathworks.com/matlabcentral/fileexchange/62074-multi-objective-particle-swarm-optimization-mopso), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2016a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Categorías
Más información sobre Particle Swarm en Help Center y MATLAB Answers.
Agradecimientos

Inspiración para: Multiple Design Options - MOPSO (MDO-MOPSO)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Versión Publicado Notas de la versión
1.3.2.0

Now the function raises a warning if the objective function is badly programmed, which is usually the issue that is reported in some comments.

1.3.1.0

ParetoFront folder is now uploaded

1.3.0.0

Optimal Pareto Fronts are updated.
Function is modified in order to return the data form the repository

1.2.0.0

More benchmark functions and optimal Pareto Fronts are implemented

1.1.0.0

Mutation operator and crowding factor for repository removing are applied.

1.0.0.0