Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds

Abstract

Evolutionary algorithms are widely used for solving multi-objective optimization problems. A prominent example is NSGA-III, which is particularly well suited for solving problems involving more than three objectives, distinguishing it from the classical NSGA-II. Despite its empirical success, the theoretical understanding of NSGA III remains very limited, especially with respect to runtime analysis. A central open problem concerns its population dynamics, which involve controlling the maximum number of individuals sharing the same fitness value during the exploration process. In this paper, we make a significant step towards such an understanding by proving tight runtime bounds for NSGA-III on the bi-objective OneMinMax ($2$-OMM) problem. Firstly, we prove that NSGA-III requires $\Omega (n^2\log (n)/\mu )$ generations in expectation to optimize $2$-OMM assuming the population size $\mu$ satisfies $n+1\le \mu =O(\log (n{)}^c(n+1))$ where $n$ denotes the problem size and $c<1$ is a constant. Apart from~\cite{opris2025multimodal}, this is the first proven lower runtime bound for NSGA-III on a classical benchmark problem. Complementing this, we secondly improve the best known upper bound of NSGA-III on the $m$-objective OneMinMax problem ($m$-OMM) of $O(n\log (n))$ generations by a factor of $\mu /(2n/m+1{)}^{m/2}$ for a constant number $m$ of objectives and population size $(2n/m+1{)}^{m/2}\le \mu \in O(\sqrt{\log (n)}(2n/m+1{)}^{m/2})$. This yields tight runtime bounds in the case $m=2$, and the surprising result that NSGA-III beats NSGA-II by a factor of $\mu /n$ in the expected runtime.

This is a preliminary version of the paper which will appear at AAAI 2026