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Multi objective optimization with genetic algorithms written in Rust exposed to python through PyO3
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andresliszt/pymoors
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This project aims to solve multi-objective optimization problems using genetic algorithms. It is a hybrid implementation combining Python and Rust, leveraging the power ofPyO3 to bridge the two languages. This project was inspired in the amazing python projectpymoo.
- Multi-Objective Optimization: Implementations of popular multi-objective genetic algorithms like NSGA-II and NSGA-III.
- Hybrid Python and Rust: Core computational components are written in Rust for performance, while the user interface and high-level API are provided in Python.
- Extensible Operators: Support for various sampling, mutation, crossover, and survival operators.
- Constraint Handling: Ability to handle constraints in optimization problems.
To install the package you simply can do
pip install pymoors
Here is an example of how to use thepymoors package to solve a small problem using the NSGA-II algorithm:
importnumpyasnpfrompymoorsimport (Nsga2,RandomSamplingBinary,BitFlipMutation,SinglePointBinaryCrossover,ExactDuplicatesCleaner,)frompymoors.typingimportTwoDArrayPROFITS=np.array([2,3,6,1,4])QUALITIES=np.array([5,2,1,6,4])WEIGHTS=np.array([2,3,6,2,3])CAPACITY=7defknapsack_fitness(genes:TwoDArray)->TwoDArray:# Calculate total profitprofit_sum=np.sum(PROFITS*genes,axis=1,keepdims=True)# Calculate total qualityquality_sum=np.sum(QUALITIES*genes,axis=1,keepdims=True)# We want to maximize profit and quality,# so in pymoors we minimize the negative valuesf1=-profit_sumf2=-quality_sumreturnnp.column_stack([f1,f2])defknapsack_constraint(genes:TwoDArray)->TwoDArray:# Calculate total weightweight_sum=np.sum(WEIGHTS*genes,axis=1,keepdims=True)# Inequality constraint: weight_sum <= capacityreturnweight_sum-CAPACITYalgorithm=Nsga2(sampler=RandomSamplingBinary(),crossover=SinglePointBinaryCrossover(),mutation=BitFlipMutation(gene_mutation_rate=0.5),fitness_fn=knapsack_fitness,constraints_fn=knapsack_constraint,duplicates_cleaner=ExactDuplicatesCleaner(),n_vars=5,population_size=32,n_offsprings=32,n_iterations=10,mutation_rate=0.1,crossover_rate=0.9,keep_infeasible=False,)algorithm.run()
In thissmall example, the algorithm finds asingle solution on the Pareto front: selecting the items(A, D, E), with a profit of7 and a quality of15. This means there is no other combination that can match or exceedboth objectives without exceeding the knapsack capacity (7).
Once the algorithm finishes, it stores apopulation attribute that contains all the individuals evaluated during the search.
pop=algorithm.population# Get genes>>>pop.genesarray([[1.,0.,0.,1.,1.], [0.,1.,0.,0.,1.], [1.,1.,0.,1.,0.], [0.,0.,0.,1.,1.], [1.,0.,0.,0.,1.], [1.,0.,0.,1.,0.], [1.,1.,0.,0.,0.], [0.,0.,1.,0.,0.], [0.,1.,0.,1.,0.], [1.,0.,0.,0.,0.], [0.,0.,0.,1.,0.], [0.,0.,0.,0.,1.], [0.,1.,0.,0.,0.], [0.,0.,0.,0.,0.]])# Get fitness>>>pop.fitnessarray([[-7.,-15.], [-7.,-6.], [-6.,-13.], [-5.,-10.], [-6.,-9.], [-3.,-11.], [-5.,-7.], [-6.,-1.], [-4.,-8.], [-2.,-5.], [-1.,-6.], [-4.,-4.], [-3.,-2.], [-0.,-0.]])# Get constraints evaluation>>>pop.constraintsarray([[0.], [-1.], [0.], [-2.], [-2.], [-3.], [-2.], [-1.], [-2.], [-5.], [-5.], [-4.], [-4.], [-7.]])# Get rank>>>pop.rankarray([0,1,1,2,2,2,3,3,3,4,4,4,5,6],dtype=uint64)
Note that in this example there is just one individual with rank 0, i.e Pareto optimal. Algorithms inpymoors store all individuals with rank 0 in a special attributebest, which is list ofpymoors.schemas.Individual objects
# Get best individualsbest=pop.best>>>best[<pymoors.schemas.Individualobjectat0x11b8ec110>]# In this small exmaple as mentioned, best is just one single individual (A, D, E)>>>best[0].genesarray([1.,0.,0.,1.,1.])>>>best[0].fitnessarray([-7.,-15.])>>>best[0].constraintsarray([0.])
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Multi objective optimization with genetic algorithms written in Rust exposed to python through PyO3