Global optimization is concerned with finding the global extremum
(maximum or minimum) of a mathematically defined function (the objective
function) in some region of interest. In many practical problems it is
not known whether the objective function is unimodal in this region; in
many cases it has proved to be multimodal. Unsophisticated use of local
optimization techniques is normally inefficient for solving such
problems. Therefore, more sophisticated methods designed for global
optimization, i.e. global optimization methods, are important from a
practical point of view. Most methods discussed here assume that the
extremum is attained in the interior of the region of interest, i.e.,
that the problem is essentially unconstrained. Some methods address the
general constrained problem. What is excluded is the treatment of
methods designed for problems with a special structure, such as
quadratic programming with negatively quadratic forms. This book is the
first broad treatment of global optimization with an extensive
bibliography covering research done both in east and west. Different
ideas and methods proposed for global optimization are classified,
described and discussed. The efficiency of algorithms is compared by
using both artificial test problems and some practical problems. The
solutions of two practical design problems are demonstrated and several
other applications are referenced. The book aims at aiding in the
education, at stimulating the research in the field, and at advising
practitioners in using global optimization methods for solving practical
problems.
