Description:

Heuristic algorithms of optimization operates on discrete or continuous domains.
Brutal force, stochastic, greedy, physically, biologically and sociologically motivated heuristic are included, used for optimum finding and compared.

Contents:

1 Sense, advantages and disadvantages of heuristic approach
2 Task complexity and time complexity of solution finding
3 Heuristics for objective function minimization
4 Global and local opima in discrete and continuous cases
5 Suboptimum solution and basin of attraction
6 Brutal force approaches: exhaustive search and random shooting
7 Naive approaches: greedy strategy and repeated local search
8 Simulated annealing with Gauss and Cauchy noise
9 Taboo approach with space or function constrains
10 Genetic model of optimization
11 Evolutionary search methods
12 Differential evolution
13 Particle Swarm Optimizarion
14 Efficiency and coparison of heuristics

Seminar contents:

1 Sense, advantages and disadvantages of heuristic approach
2 Task complexity and time complexity of solution finding
3 Heuristics for objective function minimization
4 Global and local opima in discrete and continuous cases
5 Suboptimum solution and basin of attraction
6 Brutal force approaches: exhaustive search and random shooting
7 Naive approaches: greedy strategy and repeated local search
8 Simulated annealing with Gauss and Cauchy noise
9 Taboo approach with space or function constrains
10 Genetic model of optimization
11 Evolutionary search methods
12 Differential evolution
13 Particle Swarm Optimizarion
14 Efficiency and coparison of heuristics

Recommended literature:

Key references:
[1] Martí, R., Pardalos, P. M., Resende, M. G. C. Handbook of Heuristics. Cham (Switzerland): Springer, 2018.
[2] Locatelli, M., Schoen, M. Global Optimization: Theory, Algorithms, and Applications, SIAM, Philadelphia, 2013.
Recommended references:
[3] Edelkamp, S., Schroedl, S. Heuristic Search: Theory and Applications. Waltham: Morgan Kaufmann, 2011.
[4] Yang, X-S. Nature-Inspired Metaheuristic Algorithms. 2nd edition. Cambridge: Luniver Press, 2010.
[5] Lee K. Y., Sharkawi M. A. Modern Heuristic Optimization Techniques, New York:Wiley, 2008.
[6] Horst R., Pardalos P. M. Handbook of Global Optimization., Springer, 1994.

Keywords:

algorithm, heuristics, global optimization, integer optimization, nonlinear optimization

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar