This book covers local search for combinatorial optimization and
its extension to mixed-variable optimization. Although not yet
understood from the theoretical point of view, local search is the
paradigm of choice for tackling large-scale real-life optimization
problems. Today's end-users demand interactivity with decision
support systems. For optimization software, this means obtaining
good-quality solutions quickly. Fast iterative improvement methods,
like local search, are suited to satisfying such needs. Here the
authors show local search in a new light, in particular presenting
a new kind of mathematical programming solver, namely LocalSolver,
based on neighborhood search.
First, an iconoclast methodology is presented to design and
engineer local search algorithms. The authors' concern about
industrializing local search approaches is of particular interest
for practitioners. This methodology is applied to solve two
industrial problems with high economic stakes. Software based on
local search induces extra costs in development and maintenance in
comparison with the direct use of mixed-integer linear programming
solvers. The authors then move on to present the LocalSolver
project whose goal is to offer the power of local search through a
model-and-run solver for large-scale 0-1 nonlinear programming.
They conclude by presenting their ongoing and future work on
LocalSolver toward a full mathematical programming solver based on
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