Updated 12 May 2022
This MATLAB tool offers different functionalities for multi-objective optimization:
- Offer a common interface for different solvers
- Brute force grid search (exhaustive search)
- MATLAB single-objective genetic algorithm ('ga')
- MATLAB multi-objective genetic algorithm ('gamultiobj')
- Offer an abstraction layer to the MATLAB solver
- Scaling the input variables
- Generating and filtering initial points
- Transforming high-level data structures ('struct') to low-level ('matrix')
- Generating the low-level inputs required by the solvers
- Allow vectorized and parallel evaluation of the functions
- Divide the number of points to be evaluated into chunks
- Evaluate the chunks with parallel computing ('parfor')
- The points inside a chunk are evaluated in a vectorized way
Mathematically, the following optimization problems are solved:
- Multiple variables
- Integer variables
- Upper and lower bounds
- Inequality constraints
- Equality constraints
- Non continuous objective function
- Single-objective or multi-objective goals
This tool is developed by the Power Electronic Systems Laboratory at ETH Zurich and is available under the BSD License. The code is also available on the ETH Data Archive.
Look at the example run_example.m which generates the following results:
The code is made to take advantage of optimization methods using vectorized evaluation of the objective function. Therefore, it would be easy to add support for 'patternsearch', 'particleswarm', or 'paretosearch'. Adding support for non vectorized solvers ('fmincon', 'fminbnd', or 'fminsearch') is possible but less interesting.
- Tested with MATLAB R2018b.
gads_toolboxis required (for the MATLAB solvers).
optimization_toolboxis required (for the MATLAB solvers).
distrib_computing_toolboxis required (for parfor loops)
- Compatibility with GNU Octave not tested but probably problematic.
- Thomas Guillod, ETH Zurich, Power Electronic Systems Laboratory - GitHub Profile
- This project is licensed under the BSD License, see LICENSE.md.
- This project is copyrighted by: (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod.
Thomas Guillod (2022). multi_objective_optimization_matlab (https://github.com/ethz-pes/multi_objective_optimization_matlab), GitHub. Retrieved .
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