Mauricio G. C. Resende

Publications (178) View all

• Source

  Available from: Mauricio G. C. Resende

  Article: GRASP with path relinking for the weighted MAXSAT problem

  Paola Festa, Mauricio G C Resende
  [show abstract] [hide abstract]
  ABSTRACT: A GRASP with path relinking for finding good-quality solutions of the weighted maximum satis-fiability problem (MAX-SAT) is described in this paper. GRASP, or Greedy Randomized Adaptive Search Procedure, is a randomized multistart metaheuristic, where, at each iteration, locally op-timal solutions are constructed, each independent of the others. Previous experimental results indicate its effectiveness for solving weighted MAX-SAT instances. Path relinking is a procedure used to intensify the search around good-quality isolated solutions that have been produced by the GRASP heuristic. Experimental comparison of the pure GRASP (without path relinking) and the GRASP with path relinking illustrates the effectiveness of path relinking in decreasing the av-erage time needed to find a good-quality solution for the weighted maximum satisfiability problem.
  ACM Journal of Experimental Algorithmics. ; 212(11):1-16.
• Source

  Available from: Mauricio G. C. Resende

  Dataset: manufacturing-cell-formation.tar

  Jos Fernando Gonalves, Rua Dr, Roberto Frias, Mauricio G. C. Resende
• Source

  Available from: Mauricio G. C. Resende

  Article: Biased random-key genetic algorithms with applications in telecommunications

  Mauricio G. C. Resende
  [show abstract] [hide abstract]
  ABSTRACT: This paper surveys several applications of biased random-key genetic algorithms (BRKGA) in optimization problems that arise in telecommunications. We first review the basic concepts of BRKGA. This is followed by a description of BRKGA-based heuristics for routing in IP networks, design of survivable IP networks, redundant server location for content distribution, regenerator location in optical networks, and routing and wavelength assignment in optical networks. KeywordsOptimization in telecommunications–Genetic algorithm–Biased random-key genetic algorithm–Random keys–Combinatorial optimization–Heuristics–Metaheuristics
  Top 04/2012; 20(1):130-153. · 0.87 Impact Factor
• Source

  Available from: Mauricio G. C. Resende

  Article: A biased random-key genetic algorithm for the Steiner triple covering problem

  Mauricio G. C. Resende, Rodrigo F. Toso, José Fernando Gonçalves, Ricardo M. A. Silva
  [show abstract] [hide abstract]
  ABSTRACT: We present a biased random-key genetic algorithm (BRKGA) for finding small covers of computationally difficult set covering problems that arise in computing the 1-width of incidence matrices of Steiner triple systems. Using a parallel implementation of the BRKGA, we compute improved covers for the two largest instances in a standard set of test problems used to evaluate solution procedures for this problem. The new covers for instances A 405 and A 729 have sizes 335 and 617, respectively. On all other smaller instances our algorithm consistently produces covers of optimal size. KeywordsSteiner triple covering–Set covering–Genetic algorithm–Biased random-key genetic algorithm–Random keys–Combinatorial optimization–Heuristics–Metaheuristics
  Optimization Letters 04/2012; 6(4):605-619. · 0.95 Impact Factor
• Source

  Available from: Mauricio G. C. Resende

  Article: Experiments with LAGRASP heuristic for set k-covering

  Luciana S. Pessoa, Mauricio G. C. Resende, Celso C. Ribeiro
  [show abstract] [hide abstract]
  ABSTRACT: The set k-covering problem (SC k P) is a variant of the classical set covering problem, in which each object is required to be covered at least k times. We describe a hybrid Lagrangean heuristic, named LAGRASP, which combines subgradient optimization and GRASP with path-relinking to solve the SC k P. Computational experiments carried out on 135 test instances show experimentally that by properly tuning the parameters of LAGRASP, it is possible to obtain a good trade-off between solution quality and running times. Furthermore, LAGRASP makes better use of the dual information provided by subgradient optimization and is able to discover better solutions and to escape from locally optimal solutions even after the stabilization of the lower bounds, whereas other strategies fail to find new improving solutions. KeywordsGRASP–Hybrid heuristics–Metaheuristics–Path-relinking–Lagrangean relaxation–Lagrangean heuristics–Local search–Set covering–Set multicovering–Set k-covering
  Optimization Letters 04/2012; 5(3):407-419. · 0.95 Impact Factor