We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph wit...
Combinatorial optimization problems require computing efforts which grow at least exponentially with the problem dimension. Therefore, the use of the remarkable power of massively...
Ivan De Falco, Renato Del Balio, Ernesto Tarantino...
- In this paper we present ACS, a distributed algorithm for the solution of combinatorial optimization problems which was inspired by the observation of real colonies of ants. We a...
Heuristics are an increasingly popular solution method for combinatorial optimization problems. Heuristic use often frees the modeler from some of the restrictions placed on class...
Matroid theory gives us powerful techniques for understanding combinatorial optimization problems and for designing polynomial-time algorithms. However, several natural matroid pr...
Abstract. Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identi...
Combinatorial auctions, where buyers can bid on bundles of items rather than bidding them sequentially, often lead to more economically efficient allocations of financial resource...
Abstract. We explore a new general-purpose heuristic for nding highquality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-or...
Stefan Boettcher, Allon G. Percus, Michelangelo Gr...
Many important combinatorial optimization problems can be expressed as constraint satisfaction problems with soft constraints. When problems are too difficult to be solved exactly,...
Constraint programming offers a variety of modeling objects such as logical and global constraints, that lead to concise and clear models for expressing combinatorial optimization...