Improved data clustering using multi-trial vector-based differential
evolution with Gaussian crossover
Abstract
Many algorithms have been proposed to solve the clustering problem.
However, most of them lack a proper strategy to maintain a good balance
between exploration and exploitation to prevent premature convergence.
Multi-Trial Vector-based Differential Evolution (MTDE) is an improved
differential evolution (DE) algorithm that is done by combining three
strategies and distributing the population between these strategies to
avoid getting stuck at a local optimum. In addition, it records inferior
solutions to share information about visited regions with solutions of
the next generations. In this paper, an Improved version of the
Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is
proposed and adapted for clustering data. The purpose here is to enhance
the balance between the exploration and exploitation mechanisms in MTDE
by employing Gaussian crossover and modifying the sub-population
distribution between the strategies. To evaluate the performance of the
proposed clustering, 19 datasets with different dimensions, shapes, and
sizes were employed. The obtained results of IMTDE demonstrate
improvement in MTDE performance by an average of 12%. Our comparative
study with state-of-the-art algorithms demonstrates the superiority of
IMTDE in most of these datasets because of the effective search
strategies and the sharing of previous experiences in generating more
promising solutions. Source code is available on Github:
https://github.com/parhamhadikhani/IMTDE-Clustering.