Enhance continuous estimation of distribution algorithm by variance enlargement and reflecting sampling

Estimation of distribution algorithm (EDA) is a kind of typical model-based evolutionary algorithm (EA). Although possessing competitive advantages in theoretical analysis, current EDAs may encounter premature convergence due to the rapid shrinkage of the search range and the relatively low sampling efficiency. Focusing on continuous EDAs with Gaussian models, this paper proposes a novel probability density estimator which can adaptively enlarge the variances and thus endow EDA with flexible search behavior. For the estimated probability density, a reflecting sampling strategy which can further improve the search efficiency is put forward. With these two algorithmic strategies, a new EDA variant named EDAver is developed. Experimental results on a set of benchmark problems demonstrate that EDAver outperforms conventional EDAs and can produce superior solutions in comparison with some state-of-the-art EAs.