342-0958/02 – Unconvencional Optimization Methods (NMO)

Gurantor departmentInstitute of TransportCredits10
Subject guarantordoc. Ing. Dušan Teichmann, Ph.D.Subject version guarantordoc. Ing. Dušan Teichmann, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFSIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
TEI72 doc. Ing. Dušan Teichmann, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 25+0
Part-time Examination 25+0

Subject aims expressed by acquired skills and competences

Course deals with unconvencional algorithms and its biological a physical principles. Students will have information on modern computational approaches for modeling and simulation complicated and complex transportation systems. It contains especially the problems of selected stochastic algorithms and their modification with supplemented evolutionary elements.

Teaching methods

Lectures
Individual consultations
Other activities

Summary

Course deals with stochastic algorithms nad their modifications including the evolutionaly principles.

Compulsory literature:

AFFENZELLER, M.; WAGNER, S.; WINKLER, S.; BEHAM, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. London: Chapman and Hall / CRC, 2018. ISBN 978-11-381-1427-2 SIMON, D.: Evolutionary Optimization Algorithms. New York: John Wiley&Sons, 2013. ISBN 978-04-709-3741-9 Články publikované ve vědeckých časopisech (aktuální seznam publikací obdrží doktorand před zahájením výuky).

Recommended literature:

HASSOUN, M., H.: Fundamentals of Artifical Neural Networks. Bradford Book, 2003. ISBN 978-02-625-1467-5 IBA, H.; NOMAN, N.: New Frontier in Evolutionary Algorithms: Theory and Applications. London: Imperial College Pr, 2011. ISBN 978-18-481-6681-3 MAN, K., F.; TANG, K., S., KWONG, S.: Genetic Algorithms - Concepts and Designs. London: Springer, 1999. ISBN 978-1-4471-0577-0

Way of continuous check of knowledge in the course of semester

Průběžná práce na projektovém zadání včetně provedení experimentu.

E-learning

Other requirements

Semestral project on the defined topic and its presentation before examiner.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Stochastic optimization methods based on Simulated Annealing and Tabu Search Strategies. 2. Swarm optimization algorithms and methods (PSO, ACO, GSO, FSO, BCO, BA, ABC, HBMO). 3. Optimization Problems Solving under uncertainty. 4. Advanced genetic algorithms. 5. Artifical neural networks. 6. Max-plus algebra. 7. Wawelets and its using for optimization methods. 8. Voronoi diagrams. 9. Cluster Analysis.

Conditions for subject completion

Part-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P1041D040005) Transport Systems K English Ostrava Choice-compulsory type B study plan
2021/2022 (P1041D040005) Transport Systems P English Ostrava Choice-compulsory type B study plan
2020/2021 (P1041D040005) Transport Systems K English Ostrava Choice-compulsory type B study plan
2020/2021 (P1041D040005) Transport Systems P English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner