Using the Bee Colony Optimization Method to Solve the Weighted Set Covering Problem Broderick Crawford 1,2 , Ricardo Soto 3,4 , Rodrigo Cuesta 3 , and Fernando Paredes 5 1 Universidad San Sebasti´an, Chile 2 Universidad Finis Terrae, Chile 3 Pontiﬁcia Universidad Cat´olica de Valpara´ ıso, Chile 4 Universidad Aut´ onoma de Chile, Chile 5 Escuela de Ingenier´ ıa Industrial, Universidad Diego Portales, Chile broderick.crawford.l@gmail.com, ricardo.soto@ucv.cl, rodrigo.cuesta.a@mail.pucv.cl, fernando.paredes@udp.cl Abstract. The Weighted Set Covering Problem is a formal model for many practical optimization problems. In this problem the goal is to choose a subset of columns of minimal cost covering every row. Here, we present a novel application of the Artiﬁcial Bee Colony algorithm to solve the Weighted Set Covering Problem. The Artiﬁcial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on the intel- ligent foraging behavior of honey bees. Experimental results show that our Artiﬁcial Bee Colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches. Keywords: Weighted Set Covering Problem, Artiﬁcial Bee Colony Al- gorithm, Swarm Intelligence. 1 Introduction The Weighted Set Covering Problem (WSCP) has many applications, including those involving routing, scheduling, stock cutting, electoral redistricting and oth- ers important real life situations [14]. Diﬀerent solving methods have been pro- posed in the literature for the Weighted Set Covering Problem. Exact algorithms are mostly based on Branch-and-Bound and Branch-and-Cut techniques [2,15,4], Linear Programing and Heuristic methods [6]. However, these algorithms are rather time consuming and can only solve instances of very limited size. For this reason, many research eﬀorts have been focused on the development of meta- heuristics to ﬁnd as result good or near-optimal solutions within a reasonable period of time. An incomplete list of metaheuristics for the WSCP includes Genetic Algorithms [1,3], Simulated Annealing [5], Tabu Search [7], Cultural Al- gorithms [11,10] and Ant Colony Optimization [8]. For a deeper comprehension of most of the eﬀective algorithms for the WSCP in the literature, we refer the C. Stephanidis (Ed.): HCII 2014 Posters, Part I, CCIS 434, pp. 493–497, 2014. c  Springer International Publishing Switzerland 2014