In this paper, we present a new Artificial Bee Colony algorithm to solve the non-unicost Set Covering Problem. The Artificial Bee Colony algorithm is a recent metaheuristic technique based on the intelligent foraging behavior of honey bee swarm. Computational results show that Artificial Bee Colony algorithm is competitive in terms of solution quality with other metaheuristic approaches for the...

The research on Artificial Intelligence and Operational Research has provided models and techniques to solve many industrial problems. For instance, many real life problems can be formulated as a Set Covering Problem (SCP). The SCP is a classic NP-hard combinatorial problem consisting in find a set of solutions that cover a range of needs at the lowest possible cost following certain constraint...

Given a finite set E and a family F={E1,. . . ,Em} of subsets of E such that F covers E, the famous unicost set covering problem (USCP) is to determine the smallest possible subset of F that also covers E. We study in this paper a variant, called the Large Set Covering Problem (LSCP), which differs from the USCP in that E and the subsets Ei are not given in extension because they are very large...

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.01.035 * Corresponding author. Tel.: +39 51 2093028; fax: E-mail addresses: [email protected] (Z. Na (P. Toth), [email protected] (L. Galli). In this paper we propose a new heuristic algorithm to solve the unicost version of the well-known set covering problem. The method is based on the electromagnetism metaheuris...

The set-covering problem is an interesting problem in computational complexity theory. In [1], the setcovering problem has been proved to be NP hard and a greedy heuristic algorithm is presented to solve the problem. In [2], the set-covering problem is found to be equivalent to the problem of identifying redundant search engines on the Web, and finding efficient and effective practical algorith...

In this paper we address the following probabilistic version (PSC) of the set covering problem: min{cx | P(Ax ≥ ξ) ≥ p, xj ∈ {0, 1} j ∈ N} where A is a 0-1 matrix, ξ is a random 0-1 vector and p ∈ (0, 1] is the threshold probability level. In a recent development Saxena, Goyal and Lejeune proposed a MIP reformulation of (PSC) and reported extensive computational results with small and medium si...

Genetic Algorithms provide a weak search method that scales rather badly when used in their traditional form. On the other hand, the set covering problem, when used to solve real-world problems, presents an enormous search space for genetic algorithms. A method is presented, by which genetic material is maintained for a long time within the population to allow a greater portion of the search sp...

The set covering problem (SCP) is a wellknown combinatorial optimization problem. We present a GRASP algorithm to Unicost Set Covering Problem, a special case of the Set Covering Problem where no distinction is made between covering sets. The most significant contribution of the algorithm is the incorporation of a local improvement procedure based on the heuristics to solve satisfiability probl...

We offer a fresh perspective on solving the set covering problem to near optimality with off-the-shelf methods. We formulate minimizing the gap of a generic primal-dual heuristic for the set covering problem as an integer program and analyze its performance. The empirical insights from this analysis lead to a simple and powerful primal-dual approach for solving the set covering problem to near ...