Optimizacije v inženirstvu: Reševanje problemov z metahevrističnimi metodami v okolju MATLAB
Ključne besede:
metahevristične metode, genetski algoritem (GA), algoritem rojev delcev (PSO), eno- in večkriterijska optimizacija, MATLAB, inženirske aplikacijeKratka vsebina
Skripta obravnavajo temeljne pristope optimizacije v inženirstvu s poudarkom na uporabi metahevrističnih metod, kot sta genetski algoritem (GA) in algoritem rojev delcev (PSO). Namenjena so študentom in inženirjem, ki želijo razumeti tako teoretično ozadje kot praktično implementacijo optimizacijskih algoritmov v okolju MATLAB. Vključujejo poglavja o enokriterijskih in večkriterijskih optimizacijskih problemih, obravnavajo omejitve, različne ciljne funkcije ter vizualizacijo rezultatov. Vsako poglavje vsebuje strukturirane vaje in naloge za samostojno delo, ki spodbujajo razumevanje delovanja algoritmov, oblikovanje optimizacijskih modelov in interpretacijo rešitev. Poseben poudarek je na razlagi parametrov algoritmov, primerjavi konvergence ter vplivu nastavitev na vedenje optimizacije. Skripta se zaključijo s pregledom značilnih testnih funkcij in primeri Pareto front za večkriterijsko optimizacijo. Zasnovana so tako, da tudi uporabniki brez poglobljenega matematičnega znanja lahko postopoma razvijejo intuicijo za uporabo optimizacijskih pristopov v realnih inženirskih problemih.
Prenosi
Podatki o prenosih še niso na voljo.
Literatura
Binh, T. T., & Korn, U. (1997). MOBES: A multiobjective evolution strategy for constrained optimization problems. Paper presented at the The third international conference on genetic algorithms (Mendel 97).
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.
Coello, C. A. C., Lamont, G. B., & Veldhuizen, D. A. V. (2007). Evolutionary algorithms for solving multi-objective problems: Springer.
De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems: University of Michigan.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2005). Scalable Test Problems for Evolutionary Multiobjective Optimization. A. Abraham, L. Jain, & R. Goldberg (Eds.), Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (pp. 105-145). London: Springer London.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the MHS'95. Proceedings of the sixth international symposium on micro machine and human science.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning: Addison-Wesley.
Himmelblau, D. M. (1972). Applied Nonlinear Programming: McGraw-Hill.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press google schola, 2, 29-41.4
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95-international conference on neural networks.
Kennedy, J., & Eberhart, R. (1997). A discrete binary version of the particle swarm algorithm. Paper presented at the 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation.
Kursawe, F. (1990). A variant of evolution strategies for vector optimization. Paper presented at the International conference on parallel problem solving from nature.
MathWorks Documentation. (2025). Global Optimization Toolbox User's Guide. https://www.mathworks.com/help/gads/
Mühlenbein, H., & Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm I. Continuous parameter optimization. Evolutionary Computation, 1(1), 25-49.
Rastrigin, L. A. (1974). Systems of extremal control. Nauka.
Rosenbrock, H. (1960). An automatic method for finding the greatest or least value of a function. The computer journal, 3(3), 175-184.
Simionescu, P. A. (2020). A collection of bivariate nonlinear optimisation test problems with graphical representations. International Journal of Mathematical Modelling and Numerical Optimisation, 10(4), 365-398.
Tamaki, H., Kita, H., & Kobayashi, S. (1996). Multi-objective optimization by genetic algorithms: A review. Paper presented at the Proceedings of IEEE international conference on evolutionary computation.
Vira, C., & Haimes, Y. Y. (1983). Multiobjective decision making: theory and methodology. Noth-Holland Series in System Science and Engineering, 62-109.
Wikipedia contributors. (2025). Test functions for optimization. Wikipedia. https://en.wikipedia.org/wiki/Test_functions_for_optimization
Yang, X.-S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley.
Yarpiz. (2025). Particle Swarm Optimizationvin MATLAB [Video]. YouTube. https://www.youtube.com/watch?v=sB1n9a9yxJk
Prenosi
Izdano
11.11.2025
Kategorije
Avtorske pravice (c) 2025 Univerza v Mariboru, Univerzitetna založba
Licenca
To delo je licencirano pod Creative Commons Priznanje avtorstva-Nekomercialno 4.0 mednarodno licenco.
Podrobnosti o monografski publikaciji
THEMA Subject Codes (93)
P, PB
ISBN-13 (15)
978-961-299-079-4
COBISS.SI ID (00)
Date of first publication (11)
11.11.2025
Kako citirati
Gotlih, J., & Ficko, M. (2025). Optimizacije v inženirstvu: Reševanje problemov z metahevrističnimi metodami v okolju MATLAB. Univerzitetna založba Univerze v Mariboru. https://doi.org/10.18690/um.fs.11.2025
