Solutions to most real-world optimization problems involve a
trade-off between multiple conflicting and non-commensurate
objectives. Some of the most challenging ones are area-delay
trade-off in VLSI synthesis and design space exploration,
time-space trade-off in computation, and multi-strategy games.
Conventional search techniques are not equipped to handle the
partial order state spaces of multiobjective problems since they
inherently assume a single scalar objective function.
Multiobjective heuristic search techniques have been developed to
specifically address multicriteria combinatorial optimization
problems. This text describes the multiobjective search model and
develops the theoretical foundations of the subject, including
complexity results. The fundamental algorithms for three major
problem formulation schemes, namely state-space formulations,
problem-reduction formulations, and game-tree formulations are
developed with the support of illustrative examples. Applications
of multiobjective search techniques to synthesis problems in VLSI,
and operations research are considered. This text provides a
complete picture on contemporary research on multiobjective search,
most of which is the contribution of the authors.
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