What are local minima strategies in optimization?

What are local minima strategies in optimization?

Local minima strategies are techniques used in optimization to find the best solution within a limited region of the solution space.

Can you explain how these strategies work?

Sure! They often involve methods like gradient descent, where the algorithm iteratively moves towards lower values, but can get stuck in local minima.

What are some common techniques to avoid getting stuck in local minima?

Common techniques include using momentum, simulated annealing, or employing multiple starting points to explore the solution space more effectively.

Are there specific algorithms that are better for this?

Yes, algorithms like genetic algorithms and particle swarm optimization are designed to avoid local minima by exploring the solution space more broadly.