Add MOEA/D sampler #152
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Add MOEA/D sampler #152
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…for weight vector generation
toshihikoyanase
approved these changes
Sep 5, 2024
| author: Hiroaki Natsume | ||
| title: MOEA/D sampler | ||
| description: Sampler using MOEA/D algorithm. MOEA/D stands for "Multi-Objective Evolutionary Algorithm based on Decomposition. | ||
| tags: [sampler, multiobjective] |
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A nit, but other samplers seem to use multi-objective optimization as a tag.
Suggested change
| tags: [sampler, multiobjective] | |
| tags: [sampler, multi-objective optimization] |
| ## Installation | ||
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| ``` | ||
| pip install scipy |
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This is a nit, but it might be kind to users if we also provided a common way to install the package's dependencies.
pip install -r https://hub.optuna.org/samplers/moead/requirements.txt
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| if __name__ == "__main__": | ||
| population_size = 100 | ||
| n_trials = 1000 | ||
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| mod = optunahub.load_module("samplers/moead") | ||
| sampler = mod.MOEADSampler( | ||
| population_size=population_size, | ||
| scalar_aggregation_func="tchebycheff", | ||
| n_neighbors=population_size // 10, | ||
| ) | ||
| study = optuna.create_study(sampler=sampler) | ||
| study.optimize(objective, n_trials=n_trials) |
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Not a strong opinion, but removing if __name__ == "__main__": might be simple as an example.
Suggested change
| if __name__ == "__main__": | |
| population_size = 100 | |
| n_trials = 1000 | |
| mod = optunahub.load_module("samplers/moead") | |
| sampler = mod.MOEADSampler( | |
| population_size=population_size, | |
| scalar_aggregation_func="tchebycheff", | |
| n_neighbors=population_size // 10, | |
| ) | |
| study = optuna.create_study(sampler=sampler) | |
| study.optimize(objective, n_trials=n_trials) | |
| population_size = 100 | |
| n_trials = 1000 | |
| mod = optunahub.load_module("samplers/moead") | |
| sampler = mod.MOEADSampler( | |
| population_size=population_size, | |
| scalar_aggregation_func="tchebycheff", | |
| n_neighbors=population_size // 10, | |
| ) | |
| study = optuna.create_study(sampler=sampler) | |
| study.optimize(objective, n_trials=n_trials) |
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| Q. Zhang and H. Li, | ||
| "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition," in IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec. 2007, | ||
| doi: 10.1109/TEVC.2007.892759. |
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Since you provide the doi, how about using a link to the paper?
Suggested change
| doi: 10.1109/TEVC.2007.892759. | |
| [doi: 10.1109/TEVC.2007.892759](https://doi.org/10.1109/TEVC.2007.892759). |
| parent_population: list[FrozenTrial], | ||
| neighbors: dict[int, list[int]], | ||
| ) -> dict[str, Any]: | ||
| """Generate a child parameter from the given parent population by NSGA-II algorithm. |
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A nit.
Suggested change
| """Generate a child parameter from the given parent population by NSGA-II algorithm. | |
| """Generate a child parameter from the given parent population by MOEA/D algorithm. |
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| subproblem_parent_population = [ | ||
| parent_population[i] for i in neighbors[self._subproblem_id] | ||
| ] |
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I'm not sure, but this implementation might have unexpected behavior in the case of multi-threading.
| *, | ||
| population_size: int = 100, | ||
| n_neighbors: int | None = None, | ||
| scalar_aggregation_func: str = "tchebycheff", |
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scalar_aggregation_func seems to be either tchebycheff or weighted_sum, we may be use Literal as a type hint.
Suggested change
| scalar_aggregation_func: str = "tchebycheff", | |
| scalar_aggregation_func: Literal['weighted_sum', 'tchebycheff'] = "tchebycheff", |
|
@toshihikoyanase , Thank you for your comment. |
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Motivation
There are already exist samplers for more than 3 objective optimization, such as NSGA-III, but would like to add another one
Description of the changes
Add MOEA/D sampler
TODO List towards PR Merge
Please remove this section if this PR is not an addition of a new package.
Otherwise, please check the following TODO list:
./template/to create your package<COPYRIGHT HOLDER>inLICENSEof your package with your nameREADME.mdin your package__init__.pyREADME.mdREADME.md