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Runtime Analysis of the SMS-EMOA for Many-Objective Optimization

The classic NSGA-II was recently proven to have considerable difficulties in many-objective optimization. This paper conducts the first rigorous runtime analysis in many objectives for the SMS-EMOA, a steady-state NSGA-II that uses the hypervolume contribution instead of the crowding distance as the second selection criterion. To this aim, we first propose a many-objective counterpart, the m-objective mOJZJ, of the bi-objective OJZJ, which is the first many-objective multimodal benchmark for runtime analysis. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of $O(\mu M n^k)$ iterations, where $n$ denotes the problem size (length of the bit-string representation), $k$ the gap size (a difficulty parameter of the problem), $M=(2n/m-2k+3)^{m/2}$ the size of the Pareto front, and $\mu$ the population size (at least the same size as the largest incomparable set). This result together with the existing negative result for the original NSGA-II shows that, in principle, the general approach of the NSGA-II is suitable for many-objective optimization, but the crowding distance as tie-breaker has deficiencies. We obtain three additional insights on the SMS-EMOA. Different from a recent result for the bi-objective OJZJ benchmark, a recently proposed stochastic population update often does not help for mOJZJ. It at most results in a speed-up by a factor of order $2^{k} / \mu$, which is $\Theta(1)$ for large $m$, such as $m>k$. On the positive side, we prove that heavy-tailed mutation irrespective of the number $m$ of objectives results in a speed-up of order $k^{0.5+k-\beta}/e^k$, the same advantage as previously shown for the bi-objective case. Finally, we conduct the first runtime analyses of the SMS-EMOA on the classic bi-objective OneMinMax and LOTZ benchmarks and show that the SMS-EMOA has a performance comparable to the GSEMO and the NSGA-II.

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