Level of Study
Objectives and Content
The course aims to give an overview of modern (meta)heuristic optimization methods that are suitable for solving practical optimization problems.
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:
- 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
- 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
Required Previous Knowledge
For incoming exchange students: At least 60 ECTS in Computer Science and at least 10 ECTS in mathematics
Recommended Previous Knowledge
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences
Teaching and learning methods
The teaching is given in terms of lectures and group sessions
Lectures / 4 hours per week
Group sessions/ 2 hours per week
Compulsory Assignments and Attendance
Compulsory assignments and a project.
Compulsory assignments are valid for one subsequent semester.
Forms of Assessment
The forms of assessment are:
- It is opportunity for grades on exercises, which can be included in the final grade.
- Project report.
- Oral exam
Examination Support Material
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course coordinator and administrative contact person can be found on Mitt UiB, or contact mailto:firstname.lastname@example.org Student adviser
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.
mailto:email@example.com Student adviser
T: 55 58 40 25
Type of assessment: Oral examination
- Withdrawal deadline