Metaheuristics

Objectives:

The course aims to give an overview of modern (meta)heuristic optimization methods that are suitable for solving practical optimization problems.

Content:

The course explores the metaheuristic optimization algorithms. Topics that are covered include heuristics and approximation algorithms, local search, simulated annealing, tabu search, genetic algorithms, ant-colony, particle swarm, variable neighborhood search, adaptive large neighborhood search, hybrid algorithms and mathheuristics. The course contains a wide range of practical optimization problems as case studies.

On completion of the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

• The student has a basic understanding of how metaheuristics can be used to find good enough solutions for computationally hard optimization problems.
• The student knows the fundamental properties of different metaheuristics

Skills

• The student is able to implement a metaheuristic on a given problem
• The student can explain the advantages and disadvantages of adding different components to a metaheuristic algorithm

General competence

• The student can explain for what type of problems metaheuristics can/should be used
• The student can explain the difference between the intensification and diversification in the context of metaheuristics