  As a typical model-based evolutionary algorithm (EA), estimation of
distribution algorithm (EDA) possesses unique characteristics and has been
widely applied to global optimization. However, the common-used Gaussian EDA
(GEDA) usually suffers from premature convergence which severely limits its
search efficiency. This study first systematically analyses the reasons for the
deficiency of the traditional GEDA, then tries to enhance its performance by
exploiting its evolution direction, and finally develops a new GEDA variant
named EDA2. Instead of only utilizing some good solutions produced in the
current generation when estimating the Gaussian model, EDA2 preserves a certain
number of high-quality solutions generated in previous generations into an
archive and takes advantage of these historical solutions to assist estimating
the covariance matrix of Gaussian model. By this means, the evolution direction
information hidden in the archive is naturally integrated into the estimated
model which in turn can guide EDA2 towards more promising solution regions.
Moreover, the new estimation method significantly reduces the population size
of EDA2 since it needs fewer individuals in the current population for model
estimation. As a result, a fast convergence can be achieved. To verify the
efficiency of EDA2, we tested it on a variety of benchmark functions and
compared it with several state-of-the-art EAs, including IPOP-CMAES, AMaLGaM,
three high-powered DE algorithms, and a new PSO algorithm. The experimental
results demonstrate that EDA2 is efficient and competitive.
