Optimization using metaheuristics

Description

Many interesting optimization problems cannot be solved using standard solvers dues to their size and complexity. A pragmatic approach to optimization is to use tailored computer algorithms to test a large number of solutions in order to find a good feasible solution. Such algorithms are called heuristics. Those algorithms do not guarantee to find optimal solutions but attempt to identify good solutions. There exist a number of more general algorithmic frameworks which can be applied to a wide variety of optimization problems. Those are the so-called meta-heuristics. In this course a number of these meta-heuristics will be presented: - Simulated Annealing - Genetic Algorithms/Evolutionary Algorithms - TABU search - GRASP - ALNS - ILS As this field is constantly developing, the content of the course is constantly updated. The exercises in the course will be in the computing language Julia, hence prior experience with Julia is an advantage.