Real-world engineering problems often require concurrent
optimisation of several design objectives, which are conflicting in
most of the cases. Such an optimisation is generally called
multi-objective or multi-criterion optimisation. The area of
research that applies evolutionary methodologies to multi-objective
optimisation is of special and growing interest. It brings a
solution to many yet-opened real-world problems and questions.
Generally, multi-objective engineering problems have no single
optimal design, but several solutions of equal efficiency allowing
different trade-offs. The decision maker's preferences are normally
used to select the most adequate design. Such preferences may be
dictated before or after the optimisation takes place. They may
also be introduced interactively at different levels of the
optimisation process. Multi-objective optimisation methods can be
subdivided into classical and evolutionary. The classical methods
usually aim at a single solution while the evolutionary methods
target a whole set of so-called Pareto-optimal solutions. The aim
of this book is to provide a representation of the state-of-the-art
of the evolutionary multi-objective optimisation research area and
related new trends. Furthermore, it reports many innovative designs
yielded by the application of such optimisation methods. The
contents of the book are divided into two main parts: evolutionary
multi-objective optimisation and evolutionary multi-objective
designs.
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