Parallelization

In practice, parallelization is essential and can significantly speed up optimization. For population-based algorithms, the evaluation of a set of solutions can be parallelized easily by parallelizing the evaluation itself.

This section covers various parallelization strategies available in pymoo:

Overview

• Vectorized Operations: Using NumPy matrix operations for efficient parallel computation

• Starmap Interface: Using Python’s multiprocessing starmap for threads and processes

• Joblib: Advanced parallelization with joblib’s flexible backend system

• GPU Acceleration: High-performance computing using CUDA and PyTorch

• Custom Parallelization: Implementing your own parallelization strategy