ABSTRACT

In many real life settings, high quality solutions to hard optimization problems are required in a short amount of time. Due to the practical importance of optimization problems for industry and science, many algorithms to tackle them have been developed. One important class of such solving techniques is that of metaheuristics, that include and combine constructive procedures, local search and population-based algorithms. Simulated annealing, tabu search, ant colony optimization and genetic algorithms are just a few well known examples. In this talk, I will succinctly introduce the fundamentals of metaheuristics.  The main principles will be illustrated by describing the basic metaheuristic components and by discussing how they can be combined, with the aim of providing guidelines for the design of effective and efficient solvers for optimization problems. I will conclude with an outlook to the currently available development tools and the design methodologies.