Učni načrt predmeta

Predmet:
Večkriterijsko optimiranje in načrtovanje
Course:
Multiobjective Optimization and Design
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Tehnologije znanja 1 1
Information and Communication Technologies, 3rd cycle Knowledge Technologies 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-715
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Bogdan Filipič
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
Slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Uvod: večkriterijski optimizacijski problemi, dominiranost rešitev in Pareto optimalnost, prednostni in idealni način reševanja problemov večkriterijske optimizacije in načrtovanja

Tradicionalne metode: utežena vsota kriterijev, prevedba kriterijev v omejitve, metoda epsilon omejitev

Populacijske metode: evolucijski algoritmi za večkriterijsko optimiranje, algoritem NSGA-II, algoritem DEMO, drugi populacijski algoritmi

Vrednotenje rezultatov: statistična analiza, hipervolumen, površina dosega, Pareto-skladne mere

Študije primerov: večkriterijska optimizacija in načrtovanje v znanosti, tehniki in poslovnih sistemih

Introduction: multiobjective optimization problems, solution dominance and Pareto optimality, preference-based and ideal approaches to multiobjective optimization and design problems

Traditional methods: weighted sum of objectives, transformation of objectives into constraints, epsilon constraint method

Population-based methods: evolutionary multiobjective optimization algorithms, NSGA-II algorithm, DEMO algorithm, other population algorithms

Evaluation of results: statistical analysis, hypervolume, attainment surface, Pareto-compliant metrics

Case studies: multiobjective optimization and design in science, engineering and business

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
- V. Barichard, X. Gandibleux, and v. T'Kindt, Eds. Multiobjective Programming and Goal Programming.
Springer, 2009. ISBN 978-3-540-85645-0.
- J. Branke, K. Deb, K., Miettinen, and R. Slowinski, Eds. Multiobjective Optimization: Interactive and
Evolutionary Approaches. Springer, 2008. ISBN 978-3-540-88907-6.
- A. E. Eiben, and J. E. Smith, Introduction to Evolutionary Computing, 2nd edition. Springer, 2015. ISBN
978-3-662-44873-1.
- C.-K. Goh, and K. C. Tan, Evolutionary Multi-objective Optimization in Uncertain Environments.
Springer, 2009. ISBN 978-3-540-95975-5.
- L. Wang, A. H. C. Ng, and K. Deb, Kalyanmoy, Eds. Multi-objective Evolutionary Optimisation for Product
Design and Manufacturing. Springer, 2011. ISBN978-0-85729-617-7.

Cilji in kompetence:
Objectives and competences:

Cilji predmeta so (a) predstaviti osnove večkriterijske optimizacije in načrtovanja ter matematične koncepte, potrebne za formuliranje in reševanje tovrstnih problemov, (b) predstaviti tradicionalne in populacijske metode večkriterijskega optimiranja, (c) predstaviti metodologijo vrednotenja rezultatov, (d) prikazati uporabnost metod na primerih uporabe iz prakse.

Študenti, ki bodo uspešno končali ta predmet, bodo obvladali osnove večkriterijskega optimiranja in načrtovanja in bodo usposobljeni za uporabo predstavljene metodologije pri formuliranju in reševanju problemov s tega področja ter vrednotenje rezultatov.

The course objectives are to (a) introduce the basics of multiobjective optimization and design, and the mathematical concepts needed to formulate and solve the problems of this type, (b) present the traditional and population-based methods of multiobjective optimization, (c) present the methodology of result evaluation, (d) demonstrate the application potential of the methods on use cases from practice.

The students who will successfully complete this course will master the basics of multiobjective optimization and design, and will be capable of applying the presented methodology in formulating and solving the problems from this field and evaluating the results.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- razumevanje konceptov večkriterijskega optimiranja in načrtovanja,
- obvladovanje izbranih metod in algoritmov,
- usposobljenost za njihovo uporabo v reševanju praktičnih problemov,
- usposobljenost za interpretacijo in vrednotenje rezultatov.

Students successfully completing this course will
acquire:
- understanding the concepts of multiobjective
optimization and design,
- mastering the selected methods and algorithms,
- ability to apply them in practical problem
solving,
- ability to interpret and evaluate their results.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, samostojno delo

Lectures, seminar, consultations, individual work

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Pisni ali ustni izpit
100 %
Written or oral exam
Reference nosilca / Lecturer's references:
1. VODOPIJA, Aljoša, TUŠAR, Tea, FILIPIČ, Bogdan. Characterization of constrained continuous multiobjective optimization problems: A performance space perspective. IEEE Transactions on Evolutionary Computation, 2024 (early access), doi: 10.1109/TEVC.2024.3366659.
2. VODOPIJA, Aljoša, TUŠAR, Tea, FILIPIČ, Bogdan. Characterization of constrained continuous multiobjective optimization problems: A feature space perspective. Information Sciences, 2022, vol. 607, pp. 244-262, doi: 10.1016/j.ins.2022.05.106.
3. VODOPIJA, Aljoša, STORK, Jörg, BARTZ-BEIELSTEIN, Thomas, FILIPIČ, Bogdan. Elevator group control as a constrained multiobjective optimization problem. Applied Soft Computing, 2022, vol. 115, pp. 108277-1-108277-14, doi: 10.1016/j.asoc.2021.108277.
4. KOBLAR, Valentin, FILIPIČ, Bogdan. Evolutionary design of a system for online surface roughness measurements. Mathematics, 2021, vol. 9, no. 16, pp. 1904-1-1904-18, doi: 10.3390/math9161904.
5. ZUPANČIČ, Jernej, FILIPIČ, Bogdan, GAMS, Matjaž. Genetic-programming-based multi-objective optimization of strategies for home energy-management systems. Energy, 2020, vol. 203, pp. 117769-1-117769-15, doi: 10.1016/j.energy.2020.117769.