Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering

TL;DR

This paper introduces an archive-based Gaussian Estimation of Distribution Algorithm enhanced with adaptive clustering, improving multimodal optimization by efficiently locating multiple optima and reducing premature convergence.

Contribution

It develops a novel archive-based EDA with adaptive clustering, significantly improving multimodal optimization performance over traditional GEDAs.

Findings

Enhanced ability to find multiple optima in multimodal problems.

Reduced population size leads to faster convergence.

Competitive performance on benchmark problems.

Abstract

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.