  This paper presents the main characteristics of the evolutionary optimization
code named EOS, Evolutionary Optimization at Sapienza, and its successful
application to challenging, real-world space trajectory optimization problems.
EOS is a global optimization algorithm for constrained and unconstrained
problems of real-valued variables. It implements a number of improvements to
the well-known Differential Evolution (DE) algorithm, namely, a self-adaptation
of the control parameters, an epidemic mechanism, a clustering technique, an
$\varepsilon$-constrained method to deal with nonlinear constraints, and a
synchronous island-model to handle multiple populations in parallel. The
results reported prove that EOSis capable of achieving increased performance
compared to state-of-the-art single-population self-adaptive DE algorithms when
applied to high-dimensional or highly-constrained space trajectory optimization
problems.
