  Recent decades, the emergence of numerous novel algorithms makes it a gimmick
to propose an intelligent optimization system based on metaphor, and hinders
researchers from exploring the essence of search behavior in algorithms.
However, it is difficult to directly discuss the search behavior of an
intelligent optimization algorithm, since there are so many kinds of
intelligent schemes. To address this problem, an intelligent optimization
system is regarded as a simulated physical optimization system in this paper.
The dynamic search behavior of such a simplified physical optimization system
are investigated with quantum theory. To achieve this goal, the Schroedinger
equation is employed as the dynamics equation of the optimization algorithm,
which is used to describe dynamic search behaviours in the evolution process
with quantum theory. Moreover, to explore the basic behaviour of the
optimization system, the optimization problem is assumed to be decomposed and
approximated. Correspondingly, the basic search behaviour is derived, which
constitutes the basic iterative process of a simple optimization system. The
basic iterative process is compared with some classical bare-bones schemes to
verify the similarity of search behavior under different metaphors. The search
strategies of these bare bones algorithms are analyzed through experiments.

Neural and Evolutionary Computing