Ant colony optimization is a metaheuristic which has been
successfully applied to a wide range of combinatorial optimization
problems. The author describes this metaheuristic and studies its
efficiency for solving some hard combinatorial problems, with a
specific focus on constraint programming. The text is organized
into three parts.
The first part introduces constraint programming, which provides
high level features to declaratively model problems by means of
constraints. It describes the main existing approaches for solving
constraint satisfaction problems, including complete tree search
approaches and metaheuristics, and shows how they can be integrated
within constraint programming languages.
The second part describes the ant colony optimization
metaheuristic and illustrates its capabilities on different
constraint satisfaction problems.
The third part shows how the ant colony may be integrated within a
constraint programming language, thus combining the expressive
power of constraint programming languages, to describe problems in
a declarative way, and the solving power of ant colony optimization
to efficiently solve these problems.
关于作者
Christine Solnon is Associate Professor at the University of Lyon 1 and a member of the LIRIS laboratory. She is Vice- President of the AFPC; the French association for constraint programming.
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