339 - Surrogate Optimization explained using simple python code
Surrogate optimization is a method used to solve optimization problems that are expensive or time-consuming to evaluate directly. It relies on constructing a surrogate model (also known as a metamodel) that approximates the objective function based on a limited number of evaluations. The surrogate model is then used to guide the search for the optimal solution. This approach is particularly useful when dealing with complex simulations, physical experiments, or other computationally expensive tasks.
Unlike traditional metaheuristic approaches like Particle Swarm Optimization (PSO) and Simulated Annealing (SA), surrogate optimization is not strictly a metaheuristic. It can be combined with metaheuristics or other optimization techniques to enhance their efficiency.
Surrogate optimization is often considered a Bayesian approach because it incorporates Bayesian principles in its methodology, particularly through the use of Gaussian Processes (GPs) and Bayesian optimization techniques.
To get a practical understanding, watch this video where we implement a simple example of surrogate optimization using the same objective function from our PSO tutorial ( • 321 - What is Particle Swarm Optimization ... ). We'll use a popular surrogate model called Gaussian Process (GP) and the Bayesian Optimization framework.
Code Link: https://github.com/bnsreenu/python_fo...
Video Number: 339
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