Saving local searches in global optimization
Luca Tigli · Florence Research (University of Florence) · 2020
This thesis concerns the use of local searches within global optimization algorithms. In particular, we focus our attention on the strategies to decide whether to start or not a local search from a starting point. More specifically, our aim is to avoid the waste of computational effort due to local searches which lead to already detected local minima or to local minimizers with a poor function value. Our clustering-based strategies can be easily used to solve large scale global optimization problems and to enhance the performance of any memetic algorithm in general.