Accumulative sampling for noisy evolutionary multi-objective optimization

Objective evaluation is subject to noise in many real-world problems. The noise can deteriorate the performance of multi-objective evolutionary algorithms, by misleading the population to a local optimum and reducing the convergence rate. This paper proposes three novel noise handling techniques: accumulative sampling, a new ranking method, and a different selection scheme for recombination. The accumulative sampling is basically a kind of dynamic resampling, but it does not explicitly decide the number of samples. Instead, it repeatedly takes additional samples of objectives for the solutions in the archive at every generation, and updates the estimated objectives using all the accumulated samples. The new ranking method combines probabilistic Pareto rank and crowding distance into a single aggregated value to promote the diversity in the archive. Finally, the fitness function and selection method used for recombination are made different from those for the archive to accelerate the convergence rate. Experiments on various benchmark problems have shown that the algorithm adopting all these features performs better than other MOEAs in various performance metrics.