Heuristic Search Methodologies/
Number of credits: 8 hp
Examiner: Torbjörn Larsson
Course literature: Metaheuristics: from design to implementation, E.-G. Talbi, Wiley, 2009.
Course contents: Common concepts for metaheuristics: optimization models and methods, representation, objective function, constraint handling, performance analysis. Single-solution based metaheuristics: fitness landscapes, local search, simulated annealing, tabu search, variable neighbourhood search. Population-based metaheuristics: evolutionary algorithms, swarm intelligence. Metaheuristics for multiobjective optimization: multiobjective optimization, fitness assignment strategies, performance evaluation and Pareto front structure. Hybrid metaheuristics: combining metaheuristics with mathematical programming, constraint programming, machine learning and data mining. Parallel design of metaheuristics.
Organisation: Seminars where the participants present the course topics and solutions to selected exercises from the book. Implementation projects on applications of metaheuristics.
Examination: Active participation with presentation of course topics, solutions to exercises and results of projects.
Prerequisites: Undergraduate courses in mathematics, optimization and computer science.
The course is eligible also on advanced level, that is, for master's students, see TA1015 Heuristic Search Methodologies/Heuristiska sökmetoder.
Last updated: 2022-11-15