Initial Population Influence on Hypervolume Convergence of NSGA-III

• Published in: International journal of simulation modelling Vol. 20; no. 1; pp. 123 - 133
• Main Authors: Glamsch, J., Rosnitschek, T., Rieg, F.
• Format: Journal Article
• Language: English
• Published: Vienna DAAAM International Vienna 01.03.2021
• Subjects: Algorithms; Convergence; Evolutionary algorithms; Multiple objective analysis; Mutation; Optimization; Population; Pseudorandom; Random numbers; Sampling methods
• ISSN: 1726-4529; 1996-8566; 1726-4529
• DOI: 10.2507/IJSIMM20-1-549

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.