  As a model-based evolutionary algorithm, estimation of distribution algorithm
(EDA) possesses unique characteristics and has been widely applied to global
optimization. However, traditional Gaussian EDA (GEDA) may suffer from
premature convergence and has a high risk of falling into local optimum when
dealing with multimodal problem. In this paper, we first attempts to improve
the performance of GEDA by utilizing historical solutions and develops a novel
archive-based EDA variant. The use of historical solutions not only enhances
the search efficiency of EDA to a large extent, but also significantly reduces
the population size so that a faster convergence could be achieved. Then, the
archive-based EDA is further integrated with a novel adaptive clustering
strategy for solving multimodal optimization problems. Taking the advantage of
the clustering strategy in locating different promising areas and the powerful
exploitation ability of the archive-based EDA, the resultant algorithm is
endowed with strong capability in finding multiple optima. To verify the
efficiency of the proposed algorithm, we tested it on a set of well-known
niching benchmark problems and compared it with several state-of-the-art
niching algorithms. The experimental results indicate that the proposed
algorithm is competitive.
