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Thanks ffor your answer. yes attach mutiple arms to one trial. I have tried to use it with the follwoing example def create_experiment(): ax_client = AxClient() ax_client.create_experiment( name="multi_objective_optimization", parameters=[ {"name":...

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[57], line 43 40 ax_client = create_experiment() 42 # Request a batch of 10 trials at once ---> 43 batch_trial = ax_client.create_batch_trial()...

https://ax.dev/tutorials/generation_strategy.html but this toutorial I think for just one objective function ? can I use it for batch trials in case of Multi-objective functions?

gs = choose_generation_strategy( search_space=get_branin_search_space(), use_batch_trials=True, ) #### Main optimization loop for _ in range(2): parameters, trial_index = ax_client.get_next_trial() ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate_parameters(parameters)) but how can run the main optmization to get the...

@Balandat so in this case I do not need to use choose_generation_strateg and no need to set the use_batch_trials=True if I understand you correct? my second question: is there any...

for the of MOO (in my case differnt than the tutorial since I do not have the refernce point). should I follow the same steps since there are more detaials...

@Balandat Thanks for your answer: my problem like the following I have two objective functions that I want to find the solution for them with non linear constrained and I...

thank you very much for your answer . Does this means that I can also direct use just Pareto front after the iterations loop without to set which algorithm qNEHVI...

but how can decide we that 25 iteration is ok or we need more ? and in case that we need more should we start the experiment form first trial...

``` from tensorflow.keras.preprocessing.image import ImageDataGenerator from mealpy.utils.problem import Problem from mealpy.utils.space import IntegerVar from mealpy.swarm_based.GWO import OriginalGWO from tensorflow import keras import numpy as np # Create the data generators...