Metaheuristics

ECTS Credits

10

Level of Study

Bachelor/Master. The course can be elevated to PhD level.

Teaching semester

Spring

Objectives and Content

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.

Learning Outcomes

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

Required Previous Knowledge

For incoming exchange students: At least 60 ECTS in Computer Science and at least 10 ECTS in mathematics

Recommended Previous Knowledge

INF170

Credit Reduction due to Course Overlap

None

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

None

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Reading List

The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator

Course coordinator and administrative contact person can be found on Mitt UiB, or contact mailto:studieveileder@ii.uib.no">Student adviser

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.