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Initial Population Influence on Hypervolume Convergence of NSGA-III

Title data

Glamsch, Johannes ; Rosnitschek, Tobias ; Rieg, Frank:
Initial Population Influence on Hypervolume Convergence of NSGA-III.
In: International Journal of Simulation Modelling. Vol. 20 (March 2021) Issue 1 . - pp. 123-133.
ISSN 1726-4529
DOI: https://doi.org/10.2507/IJSIMM20-1-549

Abstract in another language

A common method for solving multi-objective optimization problems are evolutionary algorithms (EA), which are utilizing an iterative population-based approach and do not need prior information about the problem to be solved. These algorithms require a variety of control parameters, e. g. the three evolutionary operators (selection, crossover and mutation), a termination criterion and the population size, which are subject of many studies. In contrast to these a less considered factor is the initialization of the first population. This paper analyses the influence of different initialization methods besides the classic sampling with a pseudo-random number generator on the convergence behaviour of the algorithm NSGA-III.
It can be shown that different sampling methods affect the convergence behaviour significantly, whereby some methods increase while others decrease the convergence speed. The results also show a strong dependency and interaction between the initialization method and the optimization problem.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Evolutionary Algorithm; Multi-Objective Optimization; NSGA-III; Sampling; Initial Population
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Engineering Design and CAD > Chair Engineering Design and CAD - Univ.-Prof. Dr.-Ing. Frank Rieg
Faculties > Faculty of Engineering Science > Chair Engineering Design and CAD > Chair Engineering Design and CAD - Univ.-Prof. Dr.-Ing Stephan Tremmel
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 09 Mar 2021 13:15
Last Modified: 09 Mar 2021 13:15
URI: https://eref.uni-bayreuth.de/id/eprint/63768