The code in this repository was used to implement and visualize the results of the multi-objective optimization (MOO) of the hydrostatic transmission (HST) with the non-dominated sorting genetic algorithm (NSGA-II) realized with pymoo library. The images folder contains the graphs produced by the plot_* methods and functions, which allows for show, save, and show and save options.

The hst_analysis module demonstrates the use of the HST and Regressor objects adapted from the effmap. The HST object is used to initialize and calculate the efficiency and performance metrics of the transmission. The Regressor is used to fit the exponential regression model to the machine speed data, and the linear regression model to the machine mass data. The models folder contains the saved, fir regression models.

The moohst module is the main script to run the optimization, to plot the key metrics of the algorithm, its resultant Pareto front and set, its convergence.