Part 10/14:
Reducing total evaluation time from weeks to days.
Decreasing the number of iterations needed to find global minima.
Enabling exploration of more extensive search spaces quickly.
For example, in a synthetic test case for vertical wells, 60 iterations with traditional Particle Swarm Optimization (PSO) enabled desired convergence, while 25 iterations sufficed with the parallel Bayesian method.
Despite its strengths, the method has limitations in scalability. The size of the covariance matrix in GP models increases quadratically with data points, limiting practical application to problems with around 10-20 parameters.
Challenges and Future Directions
To further enhance Bayesian Optimization: