TGP-MO: Treed Gaussian Processes for Solving Offline Data-Driven Multiobjective Optimization Problems

TGP-MO are tailored surrogate models for solving offline data-driven multiobjective optimization problems. These surrogates are capable of handling large datasets and are computationally inexpensive to build.

Data and Results

• Detailed results of the experiments can be found in the Results folder or the link provided below.
• The dataset used for the tests are provided in https://drive.google.com/drive/folders/1JzcKdpc9_RhfAqd2ld88RczBUVGTebDc?usp=sharing

Clone Repository

gh repo clone industrial-optimization-group/TreedGP_MOEA

Requirements:

• Python 3.7 or up

Installation Process:

• Create a new virtual enviroment for the project
• Run the following to initialize the virtual environment: pip3 install virtualenv virtualenv venv source venv/bin/activate
• Install poetry: pip3 install poetry and poetry init
• Add packages: poetry add numpy pandas matplotlib plotly sklearn scipy pygmo optproblems tqdm diversipy pyDOE joblib jupyter statsmodels GPy graphviz paramz plotly_express pymoo seaborn

Example

An example notebook can be found in the example folder solving an offline bi-objective DTLZ2 problem, 10 decision variables with 2000 samples (LHS). If you want to use the solve a problem with our framework run framework/Solve_problem.py file. The settings of the file are similar to as described in the next section.

Replicating the Experiments

Offline Dataset

• For generating the dataset we used the matlab codes in matlab_files/Initial_sampling.m and matlab_files/generate_dataset.m. The dataset is in .mat format and necessary changes should be made before using other format files. The initial dataset with 2000 samples can be found in data_template/initial_samples folder. The dataset used for the tests consisting of 2000, 10000 and 50000 samples are provided in https://drive.google.com/drive/folders/1JzcKdpc9_RhfAqd2ld88RczBUVGTebDc?usp=sharing.
• The DBMOPP problems are implemented in Matlab and are available in matlab_files folder.

Running the experiments

• For replicating the experiments in the paper run the main_project_files/StartAll_HPC.py
• The test results will be saved as pickle files in the data_folder/main_directory provided. Change the folder locations and datset for different problem instances.
• Change the parameters sample_sizes, sampling (sampling strategy), objectives (number of objectives), dims (number of decision variables), problem_testbench, problems. For running optimization using full GP, sparse GP or TGP-MO surrogates, set approaches = ["generic_fullgp","generic_sparsegp","htgp"] respectively. Kindly note that TGP-MO is referred as htgp in the codes.
• Change the nruns parameter for the number of runs for each instance you desire to run. If you want to run parallel processes set parallel_jobs for the pool size.

Evaluting the solutions with underlying objectives

• Set evaluate_data=True in the main_project_files/StartAll_HPC.py file. The DBMOPP problems are evaluted using a matlab bridge.
• Provide the data location and other parameter settings are same as described above.
• The evaluted data are stored in the data_folder/main_directory in the respective sub-folders as .csv files.

Analyzing the Results

• Run the main_project_files/Analysis_HV_RMSE_T.py file with respective data location and other parameter settings are same as described above. You will be provided with tables in .csv format comparing different approaches. The tables consisit of the pairwise p-values, scores and other details about the approaches being compared.
• Run the main_project_files/Analysis_points_used.py for generating the plots of the number of samples used for building the leaf node GPs in the TGP-MO surrogates.
• Run the main_project_files/Analysis_all_grouped_boxplt.py to generate the grouped boxplots comparing the differnt approaches.