MPŠ MP&Scaron MP&Scaron MP&Scaron Avtorji

Jožef Stefan
Postgraduate School

Jamova 39
SI-1000 Ljubljana

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10


Course Description

Multiobjective Optimization and Design


Information and Communication Technologies, third-level study programme


prof. dr. Bogdan Filipič


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.


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

Course literature:

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

Significant publications and references:

• T. Tušar, and B. Filipič. “Visualization of Pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method.” IEEE Transactions on Evolutionary Computation, vol. 19, no. 2, pp. 225-245, 2015.
• M. Mlakar, D. Petelin, T. Tušar, and B. Filipič. “GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models.” European Journal of Operational Research, vol. 243, no. 2, pp. 347-361, 2015.
• E. Dovgan, M. Javorski, T. Tušar, M. Gams, and B. Filipič. “Discovering driving strategies with a multiobjective optimization algorithm.” Applied Soft Computing, vol. 16, no. 1, pp. 50-62, 2014.
• M. Depolli, R. Trobec, and B. Filipič. “Asynchronous master-slave parallelization of differential evolution for multiobjective optimization.” Evolutionary Computation, vol. 21, no. 2, pp. 261-291, 2013.
• P. Korošec, J. Šilc, and B. Filipič. “The differential ant-stigmergy algorithm.” Information Sciences, vol. 192, no. 1, pp. 82-97, 2012.


Written or oral exam (100%)

Students obligations:

Coursework, written or oral exam