This book presents an overview of archiving strategies developed over
the last years by the authors that deal with suitable approximations of
the sets of optimal and nearly optimal solutions of multi-objective
optimization problems by means of stochastic search algorithms. All
presented archivers are analyzed with respect to the approximation
qualities of the limit archives that they generate and the upper bounds
of the archive sizes. The convergence analysis will be done using a very
broad framework that involves all existing stochastic search algorithms
and that will only use minimal assumptions on the process to generate
new candidate solutions. All of the presented archivers can effortlessly
be coupled with any set-based multi-objective search algorithm such as
multi-objective evolutionary algorithms, and the resulting hybrid method
takes over the convergence properties of the chosen archiver. This book
hence targets at all algorithm designers and practitioners in the field
of multi-objective optimization.