This work proposes a meta-learning system based on Gradient Boosting Machines to recommend local search heuristics for solving flowshop problems. The investigated approach can decide if a metaheuristic (MH) is suitable for each instance. It can also …
This work investigates the use of meta-learning for optimization tasks when a classic operational research problem (flowshop) is considered at the base level. It involves sequencing a set of jobs to be processed by machines in series aiming to …
This paper investigates the efficiency of Harmony Search based algorithms for solving multi-objetive problems. For this task, four variants of the Harmony Search algorithm were adapted in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) …