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

Stochastic Optimization Methods


Information and Communication Technologies, second-level study programme


prof. dr. Bogdan Filipič


The course objectives are to (a) to give essential knowledge on stochastic optimization methods, (b) present the types of stochastic optimization algorithms, and their advantages and drawbacks, (c) present the methodology of evaluating the results of stochastic optimization algorithms and their adaptation for solving specific types of problems, (d) show their practical potential.

The students who will successfully complete this course will master the basics of stochastic optimization and will be capable of applying stochastic algorithms in solving demanding optimization problems and following further development in this field.


Optimization, optimization problems, duality of minimization and maximization. Types of optimization: exact and stochastic, analytical and empirical, continuous and discrete, static and dynamic, single- objective and multi-objective. Optimization based on numerical models. Examples of optimization problems and sources of their difficulty.

Stochastic optimization:
Stochasticity of data and optimization procedures, motivation for stochastic optimization, advantages and disadvantages of stochastic optimization methods. Simple stochastic methods: random search and local optimization.

Stochastic optimization algorithms:
Simulated annealing. Evolutionary algorithms: genetic algorithms, evolution strategies, evolutionary programming, genetic programming and differential evolution. Tabu search, particle swarm optimization, ant colony optimization. Characteristics of the algorithms and their comparison, examples of application.

Evaluation of results:
Statistical analysis of stochastic algorithm results, performance measures and presentation of results. Differences between design and routine problems, and between synthetic test problems and real-world problems.

Applied aspects:
Setting parameter values in stochastic optimization algorithms, hybridization of algorithms, multi-objective optimization and optimization with subjective evaluation of solutions. Typical domains of application and practical case studies from design and modeling, empirical data analysis, scheduling and resource management.

Course literature:

Selected chapters from the following books:

• A. E. Eiben, and J. E. Smith. Introduction to Evolutionary Computing, 2nd edition. Springer, 2015. ISBN 978-3-662-44873-1
• A. Kaveh. Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, 2014. ISBN 978-3-319-05548-0
• F. Neumann, and C. Witt. Bioinspired Computation in Combinatorial Optimization. Springer, 2010. ISBN 978-3-642-16543-6
• G. Rozenberg, Th. Bäck, and J. N. Kok (Eds.). Handbook of Natural Computing. Springer, 2012. ISBN 978-3-540-92909-3
• E.-G. Talbi. Metaheuristics: From Design to Implementation. Wiley, 2009. ISBN 978-0-470-27858-1

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.


Seminar work (50%)
Written or oral exam (50%)

Students obligations:

Seminar work, written or oral exam.